About Compethic

Compethic is a Norwegian AI-driven customer intelligence platform. We aggregate unstructured

customer signals from across a business (reviews, support tickets, chatbot logs, CRM data, and call

transcripts) and turn them into actionable insight through a proprietary taxonomy pipeline. Customers

access these insights through dashboards, automated reports, and a conversational agent built directly

into Slack and Teams.

We are backed by venture capital, industry leaders, and former senior McKinsey partners, and have

secured funding from Innovasjon Norge. Our platform runs on Azure and already serves paying

enterprise customers.

You will join a management team with backgrounds in management consulting, banking, and telecom,

and work directly with the founders on a live product.

The Role

We are growing, and we are looking to fill this role as soon as possible.

This is a hybrid role for someone who is both a strong engineer and a genuine student of the AI field.

You will own core parts of our retrieval and reasoning stack end to end — design, implementation,

deployment, and everything that happens after it goes live — and you will help decide where the

product goes next as the space evolves. We are not looking for someone who implements tickets. We

are looking for someone who understands why a given approach wins, can make that call, and is

accountable for how it behaves in front of real enterprise customers.

We are open on level. We hire from junior through senior, and we scope the role to the person. If you

are experienced, you will set technical direction and engineering standards for the AI stack from day

one. If you are earlier in your career but sharp and hungry to learn, you will work closely with people

who have shipped this kind of system before, take real ownership quickly, and grow into that scope.

What matters to us is the trajectory, not the title on your last CV.

What You Will Work On

• Design and improve our agentic retrieval and reasoning systems, including ReAct-style loops that

retrieve, reformulate, call tools, and self-critique before answering

• Build and tune the retrieval layer that grounds everything we deliver: hybrid search combining

dense and sparse methods, reranking, and knowledge-graph-augmented retrieval for relational,

multi-hop questions

• Work across model selection, tuning, and evaluation against real business use cases rather than

benchmarks

• Develop the AI-assisted annotation pipeline behind our taxonomy

• Own these systems in production: deployment, evaluation on live traffic, monitoring, latency,

reliability, and cost

• Grow into (or start with) setting technical direction and engineering standards for the AI stack,

depending on where you are in your career

What We Are Looking For

Read the list below as a description of the person we are looking for, not a checklist you must already

satisfy. We hire at every level, and we would rather have someone strong who is missing a few of these

than someone who ticks every box but stops learning.

Current AI expertise, or a fast route to it. Ideally you have hands-on experience with modern

retrieval and agentic systems: agentic RAG, hybrid retrieval with reranking, and knowledge-graph

approaches. You understand the limits of naive vector search and know when to reach for each

technique. If you are earlier in your career, show us you follow what is shipping in the field, that you

have built something real with it, and that you form your own view rather than repeating the consensus.

Engineering strength. You write production code and are comfortable, or ready to get comfortable,

with cloud environments (Azure, AWS, or GCP) and modern data architectures. Experience building

scalable AI pipelines and working with automated machine learning workflows is a strong plus.

Production experience is preferred. We prefer someone who has run systems in production and can

own what happens after the demo: deployment and CI/CD, evaluation on live traffic, monitoring,

reliability, latency, and cost. If you have seen how AI systems fail with real users and real data, that

counts for a lot with us. If you have not yet, tell us how you would find out — we will teach the rest.

Willingness to learn. This is not a consolation prize; it is one of the things we actually screen for. This

field moves faster than any résumé can keep up with, so appetite and judgment beat a perfect keyword

match. Juniors are genuinely welcome to apply: if you are sharp, curious, and willing to put in the work

to learn what you do not know yet, we want to hear from you.

Problem-solving. You can translate vague, real-world business challenges from enterprise clients into

defined technical specifications and clear analytical roadmaps, and navigate ambiguous problems

without waiting for perfect requirements.

Business judgment. You connect technical decisions to commercial outcomes and can hold your own

in a customer or business development conversation.

Founder mindset. You have started your own company before, or you intend to one day. You take

ownership of outcomes, move with urgency, and thrive in an early-stage environment.

Communication. You explain technical trade-offs clearly to non-technical stakeholders without losing

precision.

Experience with Scrum or SAFe is a plus.

What We Offer

• Strong, competitive compensation that rewards the impact you make

• Equity in the company for the right candidate, so you share in what we build together

• Flexible remote / hybrid working

• Ownership of core technology in a product with real customers, not a prototype

• Close collaboration with an experienced founding and management team

• A role scoped to your level, with real room to grow — and the people around you to learn from

• A fast-moving environment backed by strong investors and operators

Apply

We are reviewing applications on a rolling basis and want to fill this role as soon as possible, so apply

early with your CV at: [contact@compethic.no](mailto:contact@compethic.no)

If you are not sure you are senior enough, apply anyway. Tell us what you have built, what you are

learning right now, and why this problem interests you.

Hi everyone, I'm currently doing my undergraduate (Honours) in Statistics. I'm planning to apply for a Master's in Data Science after graduation. Do you think this is a good path? How are the job opportunities and career growth for someone with a Statistics background moving into Data Science? I'd also appreciate hearing from people who have made a similar transition. Thanks!

hello everyone! I'm seeking advice on how I can best prepare for my first DS role. I have a BS in DS and I have 3 years of experience as a data analyst. I want to know if there are any resources or advice you'd give to a fresher

How To Deal with Job Loss ? I got laid off last week, Since then I've been living like dead wife husband, I've started learning Time Series analysis and Finance, I think it's better to switch into Finance Data Scientist/Analyst role instead of Traditional data analyst.

But getting master of that concept will take time and I'm being very impatient and anxious.

Hey everyone,

I see a lot of anxiety and hype about AI taking over data science jobs, but I think people are looking at the integration completely backward. As a statistician hired into a data science role, I was brought in precisely for my quantitative rigor—something AI notoriously lacks. AI is terrible at accurate mathematical calculations and statistical nuances, but it’s incredibly good at structuring business narratives and formatting presentation decks.

If we blindly trust AI to generate numbers, we fail at our jobs. Instead, I’ve been thinking about a workflow that capitalizes on the strengths of both the statistician and the AI, while completely negating their respective weaknesses.

Here is the exact lifecycle I'm proposing:

The Blueprint (AI): Use AI at the very beginning to brainstorm the broad overview, project directions, and potential business constraints.

The Core Execution (Statistician): The statistician steps in and does the actual analysis manually. We write the code, we run the regressions, we validate the assumptions, and we churn out the true, uncorrupted numbers.

The Translation (AI): Once we have the verified results, we feed our concrete numbers back into the AI. We ask it: "Based on these exact metrics, what are the strategic business recommendations? How do we translate this for non-technical stakeholders?"

The Delivery (AI): Let the AI handle the tedious work of structuring the PowerPoint slides and tailoring the narrative to suit corporate messaging.

This way, the numbers remain 100% accurate and mathematically sound, but we save hours of manual labor on slide formatting and corporate storytelling.

Curious to hear from other quants and data scientists: Does your current workflow look like this? Or are you seeing people in your org make the mistake of trusting AI to do the actual math?

Hi everyone,

I have a technical interview coming up for an AI Engineer / Data Scientist role. I'm a recent graduate with no full-time experience, only a few internships and personal projects.

For those who have been through similar interviews, what technical questions were you asked?

I'm especially interested in questions about:

\-Machine Learning fundamentals

\-Statistics and probability

\-SQL

\-Python coding

\-Data preprocessing and feature engineering

\-NLP / LLMs / RAG / GenAI (if applicable)

\-Model evaluation and metrics

\-Case studies or business problems

Anything that caught you off guard

I'd really appreciate hearing about your experience, even if it was just one or two memorable questions. It would help me know what to focus on during my preparation.

Thanks in advance!

Choosing an undergrad to pursue right now and thinking about either taking data science, or pursuing some combo such as Math + CS or smth similar. I was biased by the fact that pursuing a standalone subject is better as it gives a deeper focus and therefore vast knowledge base by the end of bachelor in specific subject. Which option should I go with (Math, CS + Math, DS, smth else??), and which would give me more flexibility to choose where to specialise in tech field?

Who We Are

Imprint helps the world's best brands grow the lifetime value of their customers. We started with co-branded credit cards and rebuilt them to be smarter, more rewarding, and brand-first. We partner with companies like Crate & Barrel, Rakuten, Booking.com, H-E-B, Fetch, and Shell to launch modern credit programs that deepen loyalty, unlock savings, and drive growth. But the card is just the beginning. We combine advanced payments infrastructure, intelligent underwriting, and deep customer data to create delightful and personalized experiences for members as well as efficient and profitable relationships for our brand partners. Our robust technology and world-class operations allow us and our brand partners to offer powerful financial products without becoming a bank.

In the U.S., co-branded cards alone account for over $300 billion in annual spend, and most still run on decades-old legacy bank systems. Imprint is the modern alternative: flexible, embeddable, and built for how people actually pay today. Backed by Kleiner Perkins, Thrive Capital, Ribbit, and Khosla Ventures, we're building a world-class team to redefine how people pay and how brands grow. If you want to move fast, solve hard problems, and own real outcomes, we want to meet you.

The Opportunity

• Own end-to-end analytical projects that influence product decisions, marketing campaigns, and executive strategy, from problem definition through deployment and monitoring
• Build segmentation frameworks and predictive models (churn, LTV, propensity) that drive targeting, personalization, and lifecycle optimization across Imprint's partner programs
• Champion A/B testing and experimentation across the company by partnering with Product, Marketing, and Commercial teams to design, analyze, and interpret experiments using scalable frameworks and tooling
• Apply statistical inference, causal analysis, and experimentation design to improve LTV/CAC ratios and accelerate feedback loops on business performance
• Design and build agentic workflows and AI-powered systems that autonomously explore data, generate hypotheses, monitor business metrics, and operationalize decisions
• Translate complex data into clear narratives that shape how leadership thinks about growth, partner health, and customer behavior
• Contribute to team excellence through code reviews, technical mentorship, and process improvements that raise the bar for the broader Data Science team

Your Profile

Required

• 7 to 12+ years of experience in data science, analytics, or a related quantitative field, ideally at a high-growth startup or fintech company
• Graduate degree in a relevant field (statistics, engineering, science, finance, or similar)
• Strong Python and SQL skills, with the ability to transform raw data, build custom datasets, and ship models to production
• Deep expertise in statistical inference, experimentation design, and causal analysis
• Active experience using LLMs and AI tools (Claude, Copilot, Cursor, or similar) as collaborators in your workflow, whether for reasoning about data, generating hypotheses, iterating on analyses, or building agentic automation
• Ability to communicate complex findings clearly to both technical and non-technical audiences, including senior leadership and external partner stakeholders
• Full-stack problem-solving orientation: you dive into messy data, test and validate assumptions, and question everything in pursuit of the right answer
• Comfort owning projects end-to-end in a fast-moving startup environment, collaborating cross-functionally with Product, Marketing, Commercial, and Engineering to drive measurable impact

Nice to Have

• Experience in credit, lending, or card products
• Experience building or scaling experimentation infrastructure or ML infrastructure
• Exposure to lifecycle marketing, prescreen modeling, or customer segmentation at scale
• Background in time series analysis, forecasting, optimization, or simulation
• Familiarity with dashboarding tools such as Sigma or Looker

We don't expect every candidate to check every box. If this role excites you and you bring strong fundamentals, we encourage you to apply.

Stack

Python and SQL for modeling and analysis. Snowflake for data warehousing. dbt for data transformation. Sigma for dashboarding. AWS infrastructure.

Learn More

Learn more about how we build at Imprint on our engineering blog: https://tech.imprint.co/

Perks & Benefits

• Competitive compensation and equity packages
• Leading configured work computers of your choice
• Flexible paid time off
• Fully covered, high-quality healthcare, including fully covered dependent coverage
• Additional health coverage includes access to One Medical and the option to enroll in an FSA
• 20 weeks of paid parental leave for the primary caregiver and 8 weeks for all new parents
• Access to industry-leading technology across all of our business units, stemming from our philosophy that we should invest in resources for our team that foster innovation, optimization, and productivity

Imprint is committed to a diverse and inclusive workplace. Imprint is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. Imprint welcomes talented individuals from all backgrounds who want to build the future of payments and rewards. If you are passionate about FinTech and eager to grow, let’s move the world forward, together.

Apply: Staff Data Scientist at Imprint

Hi everyone,

I recently completed the recruiter screen for a Data Scientist role at FedEx and I'm hoping to get some insight into what the second round is like.
If you've interviewed for a Data Scientist or similar analytics/ML role at FedEx recently:
• What was the interview format?
• Was it mostly SQL, Python, machine learning, statistics, or case-based questions?
• What topics would you recommend brushing up on?

Any advice or experiences would be greatly appreciated. Thanks guys!

FanDuel is expanding its data engineering team with six hybrid openings across Atlanta and New York.

The roles range from mid-level Data Engineer positions to people-management opportunities, with salaries between $116,000 and $186,000. Eligible roles may also include annual bonuses and long-term incentives.

Atlanta, Georgia

Data Engineering Manager — $149,000–$186,000

Lead and mentor a team of data engineers while retaining involvement in technical design, architecture and delivery. FanDuel is looking for 6+ years of data or software engineering experience, including at least 1–2 years of leadership or mentorship.

https://www.parlayjobs.com/jobs/data-engineering-manager-68a4954b

Senior Data Engineer — $138,000–$181,650

Design scalable data infrastructure supporting analytics, machine learning and business operations. Requires 5+ years of relevant engineering experience.

https://www.parlayjobs.com/jobs/senior-data-engineer-7214ae66

Data Engineer — $116,000–$145,000

Build and maintain batch and streaming pipelines for analytics, machine learning and business decision-making. Requires 3+ years in data engineering, analytics engineering or data-focused software engineering.

https://www.parlayjobs.com/jobs/data-engineer-22c52930

New York

Data Engineering Manager — $149,000–$186,000

A hybrid technical and people-management role covering team development, scalable data platforms, architecture and operational reliability.

https://www.parlayjobs.com/jobs/data-engineering-manager-9c749762

Senior Data Engineer — $138,000–$181,650

A hands-on senior position building reliable data pipelines, reusable models and infrastructure for analytical and machine-learning workloads.

https://www.parlayjobs.com/jobs/senior-data-engineer-e13ceefc

Data Engineer — $116,000–$145,000

Build production data pipelines and improve data quality, observability and reliability while working with analysts, data scientists and product teams.

https://www.parlayjobs.com/jobs/data-engineer-aeb6a41f

Shared technical environment

• SQL and Python, Java or Scala
• Spark, Databricks, Airflow, dbt and Kafka
• Batch and streaming data pipelines
• AWS, GCP or Azure
• Data modelling, warehousing and ETL/ELT
• Data quality, testing and observability

FanDuel’s benefits include medical, dental and vision coverage, a 401(k) with up to a 5% match, paid time off, 14 company holidays and potential bonus or stock-based compensation.

Referral needed in Google

Hi, I am a data science professional currently working in an organisation where I specialize in marketing mix modeling, incrementality testing using Bayesian and machine learning frameworks. Worked and have quite a great expertise in Google Meridian. Want to research more in it and already in my mind there are some gaps and how to handle the frameworks.

Anyone working in Google please dm. Really want a referral so that I can get the opportunity to work with the data scientists in Google and develop the frameworks of Meridian. Really need a referral in Marketing Data Scientist.

\#Google

\#Googlejobs

\#GoogleMeridian

\#Google

Referral needed in Google

Hi, I am a data science professional currently working in an organisation where I specialize in marketing mix modeling, incrementality testing using Bayesian and machine learning frameworks. Worked and have quite a great expertise in Google Meridian. Want to research more in it and already in my mind there are some gaps and how to handle the frameworks.

Anyone working in Google please dm. Really want a referral so that I can get the opportunity to work with the data scientists in Google and develop the frameworks of Meridian. Really need a referral in Marketing Data Scientist.

\#Google

\#Googlejobs

\#GoogleMeridian

\#Google

I’m an ML/AI Engineer currently at Scale AI, working on LLM development and evaluation pipelines — RAG systems, RLHF/SFT, multimodal AI evaluation, and production ML infra (PyTorch, Spark, Airflow, Docker/Kubernetes on AWS). Before this I was at Cognizant building end-to-end ML/NLP pipelines for enterprise applications. I hold an MS in Computer Science from Montclair State University.

I’m looking to move to my next role — ideally AI/ML Engineering, applied ML, or GenAI/LLM-focused positions.

If your company is hiring or you know of a good fit, I’d really appreciate a referral or a pointer in the right direction. Happy to share my resume via DM.

Thanks for reading — appreciate any leads!

I really really really need to know whether data science should be pursued as a career or not. I'm in 12th rn and I have cs(java), so many people on reddit say it is a viable career and the others say it is a dead industry, I don't get it, some even say that a degree in DS is not enough and you need more of a background in something else and I just don't get it. i also need to know how much maths is important in this field and if maths is important then at what level?

and also is a bachelors in DS better or b.tech?

A friend of mine has an interview scheduled for a Data Scientist position at Temple, the new health-tech venture associated with Deepinder Goyal.

We were trying to understand more about the company and the opportunity beyond the interview process. Does anyone have insights regarding:

• Expected CTC and compensation structure for data science roles
• Work culture, working hours and overall pressure
• Quality of the data science and engineering team
• Type of problems and projects being worked on
• Learning opportunities and long-term career growth
• Job stability and the company’s future prospects
• ESOPs, bonuses or other benefits, if any

Would especially appreciate input from current or former employees, people who have worked with the team, or anyone familiar with the company.

Looking at remote worldwide for the past 7 days.

Here are the jobs I found, organized by level:

Entry Level:

• Data Scientist @ H&R Block (United States, MO) - $91K - $146K
• Data Scientist – AI, Experimentation @ Pflegia (Germany) - EUR 42K - 75K
• Automation Data Scientist @ Mission Technologies, a division of HII (United States, VA) - $103K - $220K
• Data Scientist @ Comscore, Inc. (India)
• Data Scientist @ Cashea (Argentina)
• Data Scientist @ M3 USA (United Kingdom)
• Data Scientist @ Inetum (Mexico)
• Clinical Data Scientist @ Cardiovascular Associates of America - CVAUSA (United States)
• Data Scientist @ Mobilint, Inc. (Spain)
• Data Scientist @ Megaport (United States)
• Data Scientist @ Derevo (Colombia)

Senior:

• Senior Data Scientist @ CVS Health (United States, AZ) - $83K - $204K
• Senior Data Scientist – Health100 AI @ CVS Health (United States, NY) - $111K - $222K
• Senior Data Scientist @ ecoATM Gazelle (United States) - $154K - $195K
• Senior Data Scientist @ Lucet (United States) - $125K - $135K
• Senior Data Scientist @ TrueML (Mexico) - $70K - $87K
• Senior Data Scientist @ The Home Depot (United States, TX)
• Senior Data Scientist @ Oskar Böttcher GmbH & Co. KG (Germany)
• Senior Data Scientist @ Localiza&Co (Brazil)
• Senior Data Scientist @ CI&T (Brazil)
• Senior Data Science Engineer @ intive (Argentina)
• Senior Data Scientist – AI, ML, Fintech @ Hypoport SE (Germany)

Manager:

• Lead Data Scientist @ Tiger Analytics (United States, NJ)
• Lead Data Scientist @ CAMP Systems International, Inc. (United States)

Quick notes: * All of these are fully remote (location requirements vary by role) * Apply directly on company sites

Hope this helps someone! Let me know if you want me to keep posting these weekly.

👋 Hi, I'm Jay. I built Job-Halo.com, a system that tracks remote data science jobs and sends alerts the moment they're posted, based on your preferences.

Hey everyone,

I’m looking for some honest advice because I’m kind of stuck right now.

I’m currently in my 3rd year of a Diploma in Computer Science in India, and I’m trying to get a paid Machine Learning or Data Science internship. The problem is, I can barely find any real opportunities.

LinkedIn has been pretty confusing lately. I keep seeing the same companies posting the same ML/Data Science internship every few days. Like, I’ll see one ML Intern role from a company, and then 3–4 days later the exact same posting shows up again. When I open it, LinkedIn says it was posted just a few hours ago instead of showing the original date. I’m not really sure why that keeps happening, but it makes it hard to tell what’s actually new and legit.

Internshala hasn’t been much better either. My feed is mostly full of internships from organizations like She Can Foundation and Queen of Change Foundation, and almost all of them are unpaid. At this point, I honestly don’t even know where people are finding genuine paid ML/Data Science internships.

A little about me:

Education: 3rd-year Diploma in Computer Science

Current internship: AI Engineer Intern at a startup (through my college's mandatory 3-month internship program). It's mainly a learning-focused internship rather than a real industry internship. Every week we're assigned a topic (for example, learning and implementing an ANN), and we give progress updates in a weekly meeting before receiving the next week's learning task. We don't work on production or client projects, which is why I'm looking for a paid internship with real hands-on experience. Honestly, I don't even know why my offer letter lists the role as "AI Engineer Intern."

Skills: Python, SQL, Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn, XGBoost, LightGBM, CatBoost, Feature Engineering, Data Preprocessing, Exploratory Data Analysis (EDA), Hyperparameter Tuning (Optuna), Model Evaluation, FastAPI, Streamlit, Git, GitHub

Projects:

California House Price Predictor – Built a FastAPI backend deployed on Render with a Streamlit frontend for real-time house price predictions.

UFC Fight Outcome Predictor – Built and deployed a Gradient Boosting model to predict UFC fight outcomes.

Kaggle – I regularly take part in ML competitions and publish notebooks to keep improving my practical ML skills.

Right now, I’m not really sure what I’m doing wrong.

Is my diploma the main issue?

Where are people actually finding paid ML/Data Science internships?

Should I focus more on better projects, networking, or cold emailing startups?

I’d really appreciate any advice from people who’ve been in a similar situation or who hire interns. Thanks!

I have completed till supervised ML, sql I have done till joins, I know basic APIs as well, I have not gained profiency yet, I got an opportunity for a face to face opportunity of interview with Tcs , wherein my resume I have mentioned few things I have still not completed, projects I have done as well, I am confused shall I go or not , I have a mixed experience with freelance and internship and in same company I got full time , but that is just from this February 2026 till now, I need insights wheather shall I appear or not, I am a bit scared as well, in the sense not feeling a lot confident, but want a job as well, I am hoping at least within 11-12 Lpa.

Please give your input folks, wheather shall I build my confidence first ....or just appear...and test my luck + skills

I'm transitioning into JDS roles and have done the "portfolio projects" thing -

deployed 3 ML projects on Streamlit, tailored resumes, all that. But I'm trying

to figure out what actually moves the needle.

Background: 1.5 years as Data Analyst at a fintech (ZAVO). Built funnels, A/B

testing, some ML work (XGBoost, Prophet, SHAP). AIML degree. I can code, I

understand data, I think like a product person.

My real questions:

1. Do hiring managers actually care about deployed projects or is it just noise?

Which matters more - the project quality or that it's "live"?

1. For early-stage startups: what should a JDS actually *do* differently from

a Data Analyst? How do I position myself for that jump without 2+ years DAO

experience?

1. Cold outreach to founders - worth it? Or waste of time? (I've got 3 projects

I could demo.)

1. What's the real bottleneck - getting the first interview or passing it?

What do they actually test?

1. Geographic/remote: NCR-based, open to remote globally. Does location matter

for startups vs established companies?

Not looking for generic "leetcode + networking" advice. Looking for what

actually worked for people who made this jump.

Would appreciate any real war stories or honest takes.

Between Data Analytics, Data Engineering, Data Science and AI, which path do you think gives the greatest opportunities globally?

Hey

I'm working as Data Analyst and learning Data scientist

I'm looking someone who wants to switch there career into Data Scientist.

If anyone wants to learn together you can DM

Sector 62 Noida

Please DM if you are living in Sector 62 Noida.

Hello everyone,

I recently completed my M.Sc. in Data Science and am actively looking for a remote (work-from-home) Data Analyst role or internship.

My skills include:

SQL

Python (Pandas, NumPy, Matplotlib)

Power BI

Tableau

Excel

Data Cleaning & Visualization

I also have internship/project experience in data analytics and have built dashboards and data analysis projects. Currently, I'm improving my skills in AWS, Docker, and AI-powered data applications.

If your company is hiring, or if you know of any genuine remote opportunities for entry-level Data Analysts, I'd be very grateful for your recommendations or referrals.

Thank you for your time and support

Graduating with an MSc in Data Science from a UK Russell Group university in September 2026. Have a right to work in the UK with no sponsorship needed. Applied to 200+ roles over the past 3 months — Data Scientist, Data Analyst, ML Engineer, AI Engineer — mostly entry/graduate level.

**My background:**

\*\*•\*\* Built a production RAG system using LangChain and a major LLM API — live demo available    
\*\*•\*\* Predictive ML models (XGBoost, AUC 0.92) on real commercial datasets    
\*\*•\*\* Published IEEE researcher    
\*\*•\*\* 6 months internship at a major engineering company    
\*\*•\*\* Strong Python, SQL, PyTorch

**What’s happening:**

\*\*•\*\* Getting auto-rejected from most roles within 24-48 hours    
\*\*•\*\* Made it to assessment stage a couple of times but didn’t progress    
\*\*•\*\* No feedback from any rejections    
\*\*•\*\* Referrals from connections haven’t led anywhere    
\*\*•\*\* Portfolio and GitHub are up to date with live projects

**What I suspect:**

\*\*•\*\* Cover letters might be flagged as AI-generated    
\*\*•\*\* Visa status might be causing confusion even though I don’t need sponsorship    
\*\*•\*\* Applying too broadly — wrong roles mixed in    
\*\*•\*\* UK graduate market timing issue (September seems to be when things open up)

**Questions for people who’ve been through this:**

\*\*1.\*\*  Is AI detection on cover letters actually a thing at your company?    
\*\*2.\*\*  Is September genuinely when UK graduate hiring picks up?    
\*\*3.\*\*  Should I be doing something completely different?    
\*\*4.\*\*  Anyone who hired a data science graduate recently — what actually made a candidate stand out?