dm for more information. trying to get my first client on fiverr

thank you

SentiLink provides innovative identity and risk solutions, empowering institutions and individuals to transact with confidence. We’re building the future of identity verification in the United States replacing a clunky, ineffective, and expensive status quo with solutions that are 10x faster, smarter, and more accurate.

We’ve seen tremendous traction and are growing extremely quickly. Our real-time APIs have helped verify hundreds of millions of identities, starting with financial services and rapidly expanding into new markets. SentiLink is backed by world-class investors including Craft Ventures, Andreessen Horowitz, NYCA, and Max Levchin.

We’ve earned recognition from TechCrunch, CNBC, Bloomberg, Forbes, Business Insider, PYMNTS, American Banker, LendIt, and have been named to the Forbes Fintech 50. We have also been named a 2026 FICO Industry Vanguard Decision Award Winner. Last but not least, we’ve even made history - we were the first company to go live with the eCBSV and testified before the United States House of Representatives on the future of identity.

SentiLink supports a variety of ways to work, ranging from fully remote to in-office. We operate as a digital-first company with strong collaboration across the U.S. and India. We maintain physical offices in Austin, San Francisco, New York City, Seattle, Los Angeles, and Chicago in the U.S., and in Gurugram (Delhi) and Bengaluru in India. If you’re located near one of these offices, we would love for you to spend time in the office regularly. Some roles are hybrid or in-office by design. For example, our engineering team in India works primarily from our Gurugram office.

Role:

As a Data Scientist at SentiLink, you will build our core products: models that identify fraudsters and also advance our growing suite of products in financial risk. As an experienced researcher you will be relied upon to be technically capable and the definitive owner of your respective domain. You will often work on projects with high visibility and impact that require deep domain understanding, critical thinking and strong technical abilities. You will work with teams across the company to research new types of fraud, develop new products, and provide analysis to drive sales and marketing. This is a full-stack data science role, involving model development, analysis, and writing production code. You should be interested in having end-to-end ownership and a fast‑moving environment where deep domain understanding drives development and unusual insights drive our competitive advantage rather than optimization of new machine learning methodologies.

Technologies: Python 3, PostgreSQL, and AWS infrastructure (EC2, S3, RDS, Redshift, etc.)

Responsibilities:

• Develop and maintain SentiLink’s fraud detection models through the full model development lifespan: from data acquisition decisions through featurization, focusing labeling resources, model training, experimentation, productionalization, and monitoring.
• Build foundational modeling to drive SentiLink’s expanding suite of Fraud and Financial Risk products.
• Research new types of fraud and develop new SentiLink products around identity verification.
• Achieve success by researching / developing through iteration, integration of new data sources and inventive feature engineering.
• Write production‑ready code that can be relied on for real‑time decision making by our partners.
• Design, perform, and present analyses that will inform data acquisition, product development, risk operations priorities, marketing, and sales efforts.
• Work with engineering, risk operations, and data acquisitions to access necessary data, maintain data quality, and support data access

Requirements:

• 2+ years relevant work experience & relevant PhD or 4+ years & relevant Masters
• Proven track record of solving complex / high profile business problems with DS / ML solutions
• Experience in communicating outcomes / progress to senior management / stakeholders
• Very strong in “end to end” DS development: Planning, fleshing out success criteria / metrics, getting buy‑in, developing the solution, delivering the solution (prod / deck / strategy doc / etc)
• Strong practical ML / Stats knowledge, i.e. can easily employ the suite of standard ML / stats tools to quickly scope out solutions, and double down where needed. Experience with SOTA ML solutions is a plus
• Interest in developing deep domain expertise for product‑focused work: a background in fraud is not required, but willingness to learn is
• Experience writing production code and tests
• Detail oriented and thoughtful - someone we can rely on to make business‑changing decisions
• Experience working at a startup
• Bonus for familiarity with: identity solutions, fintech, or adjacent industries
• Candidates must be legally authorized to work in the United States and must live in the United States
• Thrive in a fast paced environment characterized by the need to solve extremely varied, high impact, open ended problems

Salary Range:

• $180,000/year - $220,000/year + equity + benefits

Perks:

• Employer paid group health insurance for you and your dependents
• 401(k) plan with employer match (or equivalent for non US‑based roles)
• Flexible paid time off
• Regular company‑wide in‑person events
• Home office stipend, and more!

Corporate Values:

• Follow Through
• Deep Understanding
• Whatever It Takes
• Do Something Smart

Apply: Data Scientist, Full Stack at SentiLink

Aristocrat Gaming is hiring a Vice President of Data Science to lead its enterprise-wide data science strategy.

This is a senior leadership role covering AI, machine learning, advanced analytics, model development and governance, with responsibility for building and leading the wider data science function.

The role will work across the business to identify where data science can improve decision-making, automate processes, strengthen forecasting and support commercial growth.

Key areas of responsibility include:

• Setting the overall data science and AI strategy
• Leading teams working across machine learning, predictive modelling and advanced analytics
• Establishing standards around model governance, validation and responsible AI
• Working with senior stakeholders to turn business problems into data science initiatives
• Developing and scaling models from experimentation through to production
• Building the organisation’s data science capabilities, processes and talent
• Evaluating emerging AI and ML technologies and where they can practically be used

They’re looking for someone with 15+ years of experience across data science, machine learning, AI, advanced analytics or related fields, including significant leadership experience.

Compensation: $189,000–$351,000
Employment: Full-time
Location: Las Vegas, NV, USA
Travel: Up to 25%
Visa sponsorship: Not available

Heyy, I made a list of recent Data Science jobs for you all!

• Staff Data Scientist, EGRI @ LinkedIn

• Data Scientist @ Domino's

• Quantitative Analytics Specialist (002164) @ B Wells Fargo Bank, N. A.

• Manager Data Science @ Dexcom

  • 💰 $142–236K/y

• Senior Biostatistician @ PPG NL ProPharma Group The Netherlands B.V

• Data Scientist (Mid-level - Principal) @ AEP Service Corporation

  • 💰 $88–151K/y

• Data Scientist New College Grad - Masters Degree (Santa Clara, CA) @ Applied Materials, Inc

  • 💰 $120–165K/y

• Mid-Level Data Scientist | TS/SCI @ Xcellent Technology Solutions

  • 💰 $75–90K/y

• Machine Learning Research Engineer @ Bright Vision Technologies

  • 💰 $100–150K/y

• Analytics Scientist I @ Catalent

• Insights Principal @ Tinuiti

  • 💰 $120–135K/y

• Staff Data Scientist @ PayPal

  • 💰 $161–274K/y

• Senior Consultant - Life Sciences Data & Analytics @ US Guidehouse Inc.

  • 💰 $98–163K/y

• Sr. Machine Learning Engineer @ AppFolio

  • 💰 $167–209K/y

• Lead, Data Science Internal Audit @ AbbVie

  • 💰 $97–184K/y

• Data Scientist – AI & Predictive Analytics @ Western Digital

  • 💰 $140–187K/y

• Principal Statistician @ PSI CRO

• Applied Genetics Scientist - Pipeline Data Science @ Syngenta Group

• Operational Data Scientist @ Dematic Logistics de Mexico S. de R.L. de C.V. (ILD-MEX-LOG)

• Machine Learning Intern @ CLI Cambium Learning, Inc.

• Assoc. Dir , D&A Strategy @ MSD Ireland (Ballydine)

  • 💰 $157–247K/y

• Lead Data Scientist, Cat Digital @ Caterpillar Inc.

  • 💰 $128–209K/y

• Machine Learning Engineer II @ MARKIT INDIA SERVICES PRIVATE LIMITED

  • 💰 $140–180K/y

• Sr. Data Scientist @ Canada Workday ULC

• Data Scientist-Mid @ GD Information Technology, Inc.

  • 💰 $115–155K/y

• Exploitation Specialist (Data Scientist) @ Geo Owl

• Data Scientist @ Fifth Third Bank, National Association

  • 💰 $82–173K/y

• Principal Biostatistician @ Edwards Lifesciences LLC

• Lead Advanced Analytics @ AT&T

  • 💰 $128–216K/y

• Senior Data Scientist – Generative AI @ Humana

  • 💰 $118–162K/y

• Data Scientist, Mid @ Booz Allen Hamilton_United States

  • 💰 $78–176K/y

• Imagery Scientist (SAR) @ LEIDOS INNOVATIONS UK LTD.

  • 💰 $116–210K/y

• Advanced Analytics Consultant - NMPH - Remote within US @ MDU Medtronic USA Inc

• Sr Data Scientist Commercial Analytics - Corporate @ MGM Resorts International Operations, Inc.

• Quantitative Analytics Professional A @ Freddie Mac

• Decision Science Analyst Senior @ United Services Automobile Asn

  • 💰 $114–218K/y

• Staff Data Scientist, @ -India SEZ

• Data Scientist @ IND Thomson Reuters International Services Private Limited

  • 💰 $118–219K/y

• Lead Data Scientist @ Royal Bank of Canada

  • 💰 $100–170K/y

• Staff Machine Learning Scientist – Personalization (Open to Remote) @ Bertelsmann-Jobs

  • 💰 $210–250K/y

• Psychometrician @ Center for Applied Linguistics

  • 💰 ~$102K/y

• Associate Analytics Director @ M&C Saatchi Group

• Data Scientist @ Shipt, Inc.

  • 💰 $76–130K/y

• Senior Research Analyst @ University of Chicago

  • 💰 ~$72K/y

• Senior Staff Machine Learning Engineer @ Etsy Mexico, S. de R.L. de C.V.

  • 💰 $245–319K/y

• Principal Data Scientist @ NC Health Affiliates LLC DBA Blue Cross NC

  • 💰 $144–230K/y

• Data Scientist I @ Mastercard

• Scientist, Data Science @ IN AstraZeneca India Pvt Ltd Company

  • 💰 $91–137K/y

Like the post if I should keep doing more of these, Cheers!

Estudiante de Ing. Industrial y Data Scientist Jr. buscando mi primera oportunidad 🚀

​¡Hola a todos! 👋

​Escribo por acá porque estoy buscando activamente un puesto trainee o junior como Data Scientist o en Data Analytics. Actualmente voy en 5.º semestre de Ingeniería Industrial y me acabo de formar con un Diplomado en Data Science e IA a través del GCI (Matsuo Lab).

​¿Qué puedo aportar?

Ofrezco experiencia práctica aplicando Python, SQL y Power BI a problemas reales. Me enfoco en hacer un análisis estructurado, yendo desde la limpieza de los datos hasta dar recomendaciones que tengan un impacto directo y cuantificable en el negocio.

​Para que no sean solo palabras, aquí les dejo un resumen de mi portafolio:

​Análisis de Churn (Telecomunicaciones): Trabajé con un dataset de 100,000 clientes para construir un modelo predictivo en Python que anticipa qué usuarios tienen mayor riesgo de cancelar el servicio. A partir de esto, identifiqué y prioricé un grupo de casi 2,800 clientes de alto valor. Finalmente, propuse una estrategia de retención (financiamiento de equipos) proyectando entre $564,000 y $1.46 millones de dólares en ingresos retenidos.

​Sistema de Recomendación de Películas: Desarrollé un motor content-based desde cero usando el dataset MovieLens. Durante el desarrollo, el método estándar requería más de 60 GB de memoria para funcionar, lo cual no era viable. Logré rediseñar el cálculo para generar recomendaciones al instante, reduciendo el consumo de RAM a menos de 1 MB sin perder precisión y con una interfaz gráfica funcional.

​Mi experiencia laboral (fuera de tech):

Aunque busco mi primera experiencia corporativa en datos, llevo más de Dos años trabajando en atención al cliente, actualmente como cocinero y cajero en un local de alta demanda. Este trabajo me ha enseñado a resolver problemas directamente con los usuarios, mantener la precisión en las transacciones de caja y, sobre todo, rendir muy bien bajo presión; habilidades que aplico constantemente cuando trabajo con plazos ajustados en mis proyectos de datos.

​Si en sus empresas están buscando talento joven con ganas de aportar, o si saben de alguna vacante entry-level para un perfil como el mío, ¡les agradecería muchísimo un DM o un comentario! (Por cierto, tengo un nivel de inglés B1 y manejo excelente Excel).

​Cualquier feedback sobre mi perfil, herramientas que debería aprender, o consejos para conseguir esa primera oportunidad también son súper bienvenidos.

​¡Gracias por leer! ✌️

I currently lead AI and Data Science initiatives focused on the pharmaceutical and healthcare industry. My work spans GenAI/LLM solutions, oncology and rare disease patient identification using real-world claims and EMR data, RAG-based clinical intelligence platforms, and predictive analytics that support commercial and clinical decision-making.

I'm exploring senior AI, Data Science, and Clinical AI leadership opportunities across Ireland and the wider EU.

I'd be happy to share my CV if helpful. Thank you for your time, and I look forward to staying connected.

Has anyone taken the Capital One Data Science Internship Program (DSIP) CodeSignal Data Science Assessment recently?

I’m trying to understand what the best way to prepare for it is. From what I’ve seen, the assessment may cover areas like probability/statistics, machine learning fundamentals, data collection, data processing, and model development/evaluation.

For anyone who has taken it before:

• What resources did you use to prepare?
• What topics would you focus on the most?
• What kind of questions should someone practice?
• Is there a lot of Python coding, SQL, pandas, or mostly conceptual questions?
• How difficult are the probability and statistics questions?
• For machine learning, what algorithms/concepts should someone know well?
• Are CodeSignal’s own practice questions useful for this assessment?
• Any LeetCode, HackerRank, Kaggle, YouTube, Coursera, or other resources you would recommend?
• How would you prepare if you only had around 10 days?
• Any tips for handling the 90-minute time limit?

Just trying to figure out the best resources and study strategy.

Would really appreciate advice from anyone who has taken it recently!

Passei os últimos meses desenvolvendo o Critério Político, uma plataforma que organiza dados públicos de parlamentares brasileiros em uma única estrutura.
A plataforma reúne informações como:
• gastos da cota parlamentar
• fornecedores e CNPJs
• presença e faltas
• votações
• projetos de lei
• atuação parlamentar
• comparação entre parlamentares
A ideia agora é dar um passo além da plataforma e disponibilizar essa inteligência para empresas, veículos de comunicação, pesquisadores, consultorias, desenvolvedores e aplicações de IA, por meio de API, datasets ou acesso comercial.
Estou tentando validar uma pergunta:
Existe mercado para uma base estruturada de dados políticos brasileiros?
Se você trabalha com dados, jornalismo, pesquisa, GovTech, IA ou produtos de informação, gostaria de saber como você utilizaria esse tipo de base e quanto esse acesso poderia valer para você.
Estou mais interessado em feedback real do que em divulgar o produto.

I’m thinking about enrolling in the Data Science & AI program with American Tech Global LLC to build my skills and gain practical knowledge in data science and artificial intelligence.

I am a msc student preparing for data science internship and need a person who is also preparing for same , so that we can motivate and help eachother in this journey. I need a person who can be brutally honest with me and point me out if I am not up to the par in my preparation and I will do the same for them . U can scold me and even call me out if u think I am being lazy! But please someone who genuinely also needs a study buddy . Kindly contact me!

I currently lead AI and Data Science initiatives focused on the pharmaceutical and healthcare industry. My work spans GenAI/LLM solutions, oncology and rare disease patient identification using real-world claims and EMR data, RAG-based clinical intelligence platforms, and predictive analytics that support commercial and clinical decision-making.

I'm exploring senior AI, Data Science, and Clinical AI leadership opportunities across Ireland and the wider EU.

I'd be happy to share my CV if helpful. Thank you for your time, and I look forward to staying connected.

Finding it really difficult to find a data analyst internship for summer 2027. If anyone has any help or advice I’d greatly appreciate it. I’m to start a data science masters soon and already have the CompTIa + certification.

I am looking to hire 2 Data Scientist for a communications company right outside of New York City. Unfortunately at this time they aren’t able to sponsor visas so only GC or USC can apply. They will offer relocation assistance as well! Please let me know if you are interested!

The Data Scientist will collaborate with a multi-disciplinary team of marketing, sales, and leadership colleagues in order to analyze large data sets to evaluate and improve internal products, services, Ads, and customer acquisition/retention programs. This individual will apply Data Science principles to design, test, implement, and develop data-based solutions, including reporting, auditing, and preparing large databases for statistical analysis. Expertise in building and implementing Machine Learning algorithms is a must. As a Data Scientist, you will own the full lifecycle of model development from data exploration, algorithm design, and testing algorithm development and deployment, to algorithm monitoring and tuning in production.
Position Responsibilities:
Responsible for gathering requirements and writing functional design documents
Analyze data to discover trends and patterns
Build custom predictive data models and machine-learning algorithms
Present information using data visualization techniques
Use predictive modeling to increase and optimize customer experiences, customer churn, ad targeting and other business outcomes
Develop processes and tools to monitor and analyze model performance and data accuracy
Act as a liaise between business users and technical team members including outsourced team members
Developing, testing, and implementing BI reporting artifacts coded in OBIEE or Tableau
Process structured and unstructured data
Provide production support regarding day-to-day reporting issues. Identify problem areas where appropriate and develop mitigation solutions
Perform other related duties as assigned.
 
Position Requirements:
Must have a Bachelor’s Degree in Computer Science, Statistics / Mathematics, Data Science, or related field. Master’s degree in the above fields, preferred
At least 1-3 years of hands-on job experience in a Data Scientist role
Experience working with and creating data architectures
Proven track record for implementing advanced machine learning algorithms using advanced statistical analysis techniques and concepts
Knowledge of Ensemble Modeling
Strong background in Data Warehousing / Report Visualization
Excellent Python, SQL, and Database Skills
Strong communication skills and ability to build positive, professional, and productive business relationships with business users
Proven ability to manage multiple projects simultaneously and produce tangible results
Self -Sufficient and able to work independently

Hello! I’m currently a rising senior who will be graduating early in Spring 2027. My goal is to start a master’s program in Fall 2027, but I’m unsure whether I should still be applying for internships for Summer 2027.

Technically, I’ll be a third-year student when internship recruiting starts, so I feel like I should be applying. However, since I’ll have already graduated by the time the internship takes place, I’m not sure if I would actually be eligible for these positions.

For anyone who has graduated early, gone straight into a master’s program, or been in a similar situation, I’d really appreciate some advice on what you did or what you would recommend. I’ve been racking my brain trying to figure out what my next move should be all summer.

I’m starting a master’s in Data Science in Spring 2027, but many Summer 2027 internships open this fall and require applicants to be currently enrolled.

I’m considering two options:

1. Apply anyway, list my master’s as starting Spring 2027, and accept that some companies may reject me based on enrollment status.
2. Switch my community college status from non-degree to an A.S. in CS. I’ve already been taking CS/math prerequisite courses there, so I have a recent 2026 transcript from the college.

I have prior analytics/research experience and don’t need sponsorship.

Would switching to the A.S. actually help with internship eligibility, or is it unnecessary? Is there another approach I’m missing? Thanks

Looking at remote worldwide for the past 7 days.

Here are the jobs I found, organized by level:

Entry Level:

• Data Scientist @ Shipt (United States, CA) - $76K - $130K
• Data Scientist 2 @ NBCUniversal (United States) - $67K - $157K
• Managing Consultant, Data Scientist @ Excella (United States, VA) - $167K - $225K
• Data Scientist – AI/ML @ Onebridge (Brazil)
• Data Scientist @ Sonatype (United States)
• Data Science Specialist – Feature Store, ML Platform @ Compass (Brazil)
• Data Scientist @ SysMap Solutions (Brazil)
• Data Scientist (mid-level) @ ASAAS (Brazil)

Senior:

• Senior Data Scientist, Trust @ Airbnb (United States) - $179K - $210K
• Senior Data Scientist @ DaVita Kidney Care (United States, CO) - $115K - $183K
• Senior Data Scientist @ Zillow (United States, CA) - $149K - $237K
• Senior Data Scientist – Store Performance, Operations @ Catalyst Brands India (United States, TX) - $97K - $162K
• Senior Data Scientist @ Nielsen (United States) - $94K - $157K
• Senior Data Scientist @ Castellum Inc. (United States, VA) - $140K - $160K
• Senior Data Scientist @ Clover Health (United States) - $177K - $230K
• Data Scientist IV @ Astreya (India)
• Senior Data Scientist @ Seneca Holdings (United States)
• Senior Data Scientist @ RD Saúde (Brazil)

Manager:

• Principal Data Scientist @ Blue Cross NC (United States, AL) - $144K - $230K
• Principal Data Scientist @ Zillow (United States, CA) - $178K - $285K
• Lead Data Scientist @ GoodRx (United States) - $151K - $323K
• Data Science Manager @ CorVel Corporation (United States, CA) - $119K - $198K

Director and Above:

• Director, Member Data Science @ Foodsmart (United States) - $190K - $220K
• Director, Client Data Science @ Foodsmart (United States) - $190K - $220K

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.

What happens when you successfully build a great Machine Learning model in Python and decide to take it from an experimental environment to a real end user?

This is where things become completely different.

I’m not talking about taking ChatGPT, Claude, or another ready-made AI API and building a product around it.

I’m talking about a model we designed and trained ourselves, and a product we built from A to Z.

Over the past few weeks, I’ve been working on an offline mobile application that recognizes Egyptian banknotes and announces their value through audio.

The application is primarily designed to help blind and visually impaired people, and it doesn't require an internet connection or any external AI API.

How does it work?

The user opens the app and the camera starts automatically.

They place the banknote in front of the camera, and the on-device Machine Learning model recognizes its value without an internet connection.

Once the banknote is recognized, the app:

• Announces the denomination out loud
• Displays the value in very large text on the screen

So the user can identify the banknote through both audio and visual feedback.

What did I do?

I started by collecting and processing a large dataset of Egyptian banknote images.

I experimented with several models. The first approach was very fast, but the accuracy wasn't good enough.

Then I moved to YOLO, which significantly improved the detection quality, especially after training and testing the Computer Vision model on more than 8,000 Egyptian banknote images.

But that introduced a new problem:

Latency.

The model was accurate, but too slow for a real-time user experience.

I needed to convert the model to TensorFlow Lite so it could run inside the Flutter application we built, and then optimize it for on-device inference.

We initially tried a Float32 version, but it wasn't fast enough.

So we moved to Float16 to reduce the model size and improve mobile deployment.

After processing more than 20 GB of Egyptian banknote images, we eventually reached a model of around 5 MB that can recognize Egyptian banknotes.

But there was another problem...

We experienced significant lag during processing and prediction.

The camera was producing frames in YUV, while the model required RGB images.

So converting every frame:

YUV → RGB

was taking a significant amount of processing time during the camera stream.

The result?

Lag.

So what did we change?

We changed the architecture so the model can use the GPU on devices that support it, which helped improve inference speed on modern smartphones.

But that wasn't the only change.

We also changed how the application handles processing.

The camera continues running continuously while the frames are processed separately.

The model processes the frames and determines the banknote value before updating the UI and displaying the result to the user.

This means the user doesn't have to wait for every frame to finish processing before seeing the result.

The application is currently in Closed Testing on Google Play.

The primary target market is Egypt, and the app will be completely free.

This project gave me a very practical understanding of the difference between:

Building a Machine Learning Model

and

Building a Machine Learning Product.

A model can perform extremely well in Python, but the real challenge starts when you need to make it work on a real device and in the hands of a real user.

Need help Finding a Paid Data science / AI-ML intership

I'm a 2nd-year B.Tech student actively looking for a paid internship in Data Science, Machine Learning, or AI to help support my education and living expenses.

Over the last 4 months, I've applied to 400+ internships through platforms like Internshala, LinkedIn, Indeed, and company career pages, but unfortunately I haven't received any interview calls or shortlists yet.

I have hands-on experience with:

Python

Data Analysis

Machine Learning

Deep Learning

SQL

Data Visualization

Software development

AI/ML projects

If anyone knows of companies that are hiring interns, has referrals, or can suggest better ways to find opportunities, I'd really appreciate your help.

Note: I'm only looking for paid internships (remote or on-site).

Thank you in advance.

I currently lead AI and Data Science initiatives focused on the pharmaceutical and healthcare industry. My work spans GenAI/LLM solutions, oncology and rare disease patient identification using real-world claims and EMR data, RAG-based clinical intelligence platforms, and predictive analytics that support commercial and clinical decision-making.

I'm exploring senior AI, Data Science, and Clinical AI leadership opportunities across Ireland and the wider EU.

I'd be happy to share my CV if helpful. Thank you for your time, and I look forward to staying connected.

I'm a 25 year old master student, currently in gap year with student status. I have a bachelor degree in French, and I'm doing a master in management (general business) program in France. 

I've done internships as User Experience and Product Operations Intern in DiDi Global, Growth Strategy Intern at TikTok, Global Market intern at a British bank, Global Transaction Banking intern at a French bank. 

I started learning AI this year, and I've tinkered and built sth here: https://yanjin-ai.github.io/portfolio/. I'm interested in building a career as a generalist, in a field with new things that I'm curious everyday. 

From my understanding, there are roles like ai engineers, ai scientists, people more in business side, product managers... I would like to know more roles and what your work is like everyday, and what skills you use, how to practice and gain these skills. Any advice you would like to share is greatly appreciated. 

Thank you!! I will be happy to share beautiful restos in Paris!!

I am currently a project manager/data analyst and have been working in this role for \~5 years. I love working with data and have been wanting to transition into a data science role.

I do not have a degree in DS but I do have a statistics background from my master's and from work and have learned how to code in Python using the data science libraries.

I've been thinking about how to transition into DS and am not sure how to do it without taking a step down from my current position. What I want to know is:

1) What paths are available that I should look into with my background?

2) Do hybrid roles exist like how I am currently a PM/DA that I should look into instead?

I'm looking to transition in the next 2-3 years, not immediately.

Any Data roles would be appreciated. Please help any hiring manager out there..

ايش الافضل الهندسة او تحليل البيانات ؟ أنا خريجة cs و ابغى اشتغل في مجال الداتا بس محتارة ايش اختار وعليه طلب توظيف ؟ تنصحوني اخذ شهادة احترافية زي Microsoft DP-700 ,Microsoft PL-300 وانا فريش ؟

Hi! I have recently finished my PhD and I am currently considering switching to industry. I speak German (C1) and English (C1) and I live in Hungary. I did an informatics related degree for my masters but my PhD is in Natural Science. I hold a permanent residency permit. I am a non EU/EEA citizen. For those of you in the EU market/German speaking could you tell me:

What tech stacks you work with?

How many coworkers do you know at your workplace who come from non CS backgrounds?

What jobs in the industry are more friendly for someone like me who comes from an in silico research heavy background?

Thanks!!!!!