Requirements:
4+ years of practical analytics experience in big data environments (preferably in a B2C company).
A hands-on, passionate individual who enjoys diving deep into algorithms, data, and business problems.
Excellent analytical skills with experience querying large, complex datasets.
Strong understanding of statistical methods and significance testing.
Hands-on experience with machine learning algorithms and Python.
Experience working with modern AI approaches, including applied machine learning and familiarity with generative AI tools.
BSc (or higher) in Mathematics, Statistics, Engineering, Computer Science, or a related quantitative field.
Self-learner, able to quickly adapt to new technologies and AI-driven workflows.
Independent, proactive problem-solver with strong ownership.
Excellent communication skills, both written and verbal.
Familiarity with the ad-tech industry - a big plus.
Experience implementing real-time machine learning or data-driven systems at scale - a big plus.
Experience integrating AI models into production environments or decision-making systems - a plus.
Fun to work with and a strong team player.

Requirements:
Two years of experience in leading a team.
4+ years of practical analytics experience in big data businesses (preferable - B2C company).
A hands-on, passionate person who likes to get to the bottom of things in terms of algorithms, data, and business.
Excellent analytical skills with experience in querying large, complex data sets.
Strong understanding of statistical significance.
Hands-on experience with machine learning algorithms; Python and data visualization tools such as Tableau, Looker, etc.
BSc degree (or higher) in Mathematics, Statistics, Engineering, Computer Science, or any other quantitative field.
Self-learner, multi-tasker, able to work independently.
Self-sufficient, proactive problem-solver.
Excellent communication skills, both written and verbal.
Familiarity with the ad-tech industry - a big plus.
Experience in implementing real-time machine learning and data mining algorithms in large-scale environments - a big plus.
Fun to work with and a team player :)

Requirements:
A proven track record as an experienced architect who leads by example, capable of writing core, production-critical code, conducting rigorous architecture reviews, and mentoring senior technical staff.
At least 3 years of hands-on experience building, scaling, and optimizing Agentic LLM systems. This includes deep familiarity with common frameworks (e.g., LangChain, LangGraph, AutoGen) and a sophisticated understanding of tool-use optimization, memory management, and deterministic behavioral alignment.
At least 5 additional years of deep experience in data science, with expert-level command of classical machine learning algorithms applied to complex, real-world data landscapes.
At least 5 additional years of experience as a software engineer with high proficiency in Python and querying languages. You possess deep knowledge of software design patterns, concurrency, CI/CD pipelines, and writing clean, scalable, maintainable code.
BSc in Machine Learning, Computer Science, Electrical Engineering, Physics, Statistics, Applied Mathematics, or a related field from a top university. An advanced degree is highly preferred.
Demonstrated expertise in deploying and operating ML/LLM workloads at enterprise scale. You have a comprehensive understanding of containerization (Docker, Kubernetes), high-throughput data pipelines, and proactive system monitoring.
Exceptional ability to run end-to-end research-to-production initiatives, showcasing an analytical mindset capable of translating raw data into highly accurate, real-time defensive actions.
Extensive, hands-on experience utilizing, fine-tuning, and optimizing open-source generative AI frameworks and libraries (e.g., Hugging Face, PyTorch, vLLM, DeepSpeed) to build and deploy high-performance models.
Proven experience architecting data solutions and working within major cloud and big data ecosystems (e.g., GCP, AWS, Snowflake) to ingest, process, and analyze high-velocity, petabyte-scale datasets.

Requirements:
A proven track record as an experienced architect who leads by example, capable of writing core, production-critical code, conducting rigorous architecture reviews, and mentoring senior technical staff.
At least 3 years of hands-on experience building, scaling, and optimizing Agentic LLM systems. This includes deep familiarity with common frameworks (e.g., LangChain, LangGraph, AutoGen) and a sophisticated understanding of tool-use optimization, memory management, and deterministic behavioral alignment.
At least 5 additional years of experience as a software engineer with high proficiency in Python and querying languages. You possess deep knowledge of software design patterns, concurrency, CI/CD pipelines, and writing clean, scalable, maintainable code.
BSc in Machine Learning, Computer Science, Electrical Engineering, Physics, Statistics, Applied Mathematics, or a related field from a top university. An advanced degree is highly preferred.
Demonstrated expertise in deploying and operating ML/LLM workloads at enterprise scale. You have a comprehensive understanding of containerization (Docker, Kubernetes), high-throughput data pipelines, and proactive system monitoring.
Exceptional ability to run end-to-end research-to-production initiatives, showcasing an analytical mindset capable of translating raw data into highly accurate, real-time defensive actions.
Extensive, hands-on experience utilizing, fine-tuning, and optimizing open-source generative AI frameworks and libraries (e.g., Hugging Face, PyTorch, vLLM, DeepSpeed) to build and deploy high-performance models.
Proven experience architecting data solutions and working within major cloud and big data ecosystems (e.g., GCP, AWS, Snowflake) to ingest, process, and analyze high-velocity, petabyte-scale datasets.
Outstanding communication and presentation skills, with a demonstrated ability to align cross-functional engineering, product, and research teams toward unified technical goals

Requirements:
Education: MSc or PhD in Data Science, Computer Science, Mathematics, Statistics, or a related engineering field.
Experience: 4+ years of proven experience as a Data Scientist solving complex business problems with state-of-the-art algorithms.
Deep Learning: Hands-on experience with deep learning frameworks (e.g., PyTorch, TensorFlow, Keras).
Engineering & Production: High-level software engineering expertise, with a strong track record of deploying, maintaining, and scaling models in rigorous production environments.
Strong analytical thinking: Take a vague business question, translate it into a testable hypothesis, build the infrastructure to run an experiment, and draw rigorous conclusions from the results.
Massive Scale: Experience working with billions of records and big-data frameworks (e.g., Spark, Ray, Dask, Airflow).
Domain Expertise: Proven experience building and optimizing applied recommendation systems.
AI development: Fluency with modern AI coding tools and practices (e.g. Cursor, Claude Code, Copilot) and an eagerness to integrate them into your daily workflow
Leadership & Soft Skills: Demonstrated experience leading machine learning projects. You are a highly analytical team player capable of handling multiple tasks simultaneously in a fast-paced environment.
You might also have
LLM & Agentic Frameworks: Experience building applications with modern AI tools and frameworks (e.g., LangChain, LangGraph).
AI Observability: Hands-on experience with LLM observability, evaluation, and tracing frameworks (e.g., Langfuse).
MLOps & Infra: Experience working with MLOps tools (e.g., MLflow, Kubeflow, Weights & Biases, SageMaker) and feature stores (e.g., Feast, Tecton).
Additional information
Relocation support is not available for this position
Work visa/immigration sponsorship is not available for this position
Benefits
At our company, we want our team members to thrive. We offer a wide range of benefits designed to support well-being and work-life balance.

Requirements:
Minimum qualifications
5+ years of experience in leading data science and machine learning projects, with significant hands-on work building LLM-based applications or AI agents.
Deep practical experience with LLMs (prompt engineering, fine-tuning, model selection, and understanding trade-offs across providers and model families).
Knowledge of programming languages that are used in AI research, such as Python, and experience with AI frameworks (e.g., Hugging Face, LangChain, OpenAI, scikit-learn, TensorFlow, PyTorch).
An M.S. or Ph.D. degree in computer science, statistics, or related field OR equivalent work experience.
Preferred qualifications:
Experience with distributed cloud systems and hands-on familiarity with cloud-native architectures at scale.
Strong knowledge of deep learning models and common model architecture such as transformer models.
Knowledge of cybersecurity principles, attack vectors, and defense mechanisms.
Excellent communication (both written and verbal) and presentation skills.
Ability to work independently in a fast-paced environment and come up with creative solutions to challenging problems.

Requirements:
M.Sc in quantitative discipline (preferably in Data Science, Computer Science, Mathematics, Statistics, or another related field with a strong emphasis on quantitative analysis).
3+ years of experience in developing and deploying ML models in a production environment.
Knowledge of Data Science techniques, algorithms, and processes.
Excellent analytical and algorithmic skills. Being able to conduct rigorous evaluation, infer conclusions and creatively offer solutions based on data analysis.
Strong ownership and independence skills. Thrive in some tasks as the sole ML expert in a squad, comfortable taking ownership over the full life cycle of models and integrating versatile tasks (ML, data analysis, and Backend).
Excellent teamwork skills.
Effective communication skills to explain complex topics in English.

Requirements:
4+ years of experience building machine learning solutions end-to-end, from research and experimentation through production.
MSc or PhD in Computer Science, Mathematics, Statistics, Engineering, or a related quantitative field.
Experience developing machine learning models for real-time bidding (RTB), recommendation systems, ranking, pricing, or other large-scale optimization problems.
Strong programming skills in Python and experience working with production-grade machine learning systems.
Strong foundation in machine learning, statistical modeling, experimentation, and data analysis.
Bonus points if you have:
Experience with deep learning models in large-scale recommendation or advertising systems.
Experience using AI development tools (such as Claude Code or Cursor) to accelerate experimentation and development.

Requirements:
Bachelor's or Master's degree in Computer Science, Mathematics, Statistics, or a related field.
Minimum of 5 years of experience as a Data Scientist or in a related field, with experience working in a production environment or PHD with relevant experience
Strong programming skills in Python and proficiency in SQL.
Deep understanding of statistical analysis, machine learning, deep learning and data visualization techniques.
Strong communication and collaboration skills to work effectively in a team environment.
Skills in developing AI agents, such as LLM-based assistants or autonomous workflows - advantage.

Requirements:
Experience: Minimum of 3+ years of applied Data Science experience with a proven track record across fintech domains, with experience in fraud, risk, or payments preferred.
Production Expertise: Proven, hands-on experience deploying and maintaining machine learning models in high-traffic production environments is required, with real-time experience preferred.
Data Science Tech Stack: Expert-level Python programming (Pandas, NumPy, Scikit-Learn, XGBoost/LightGBM) and exceptional SQL skills for querying massive, complex datasets.
Data Environment: Robust experience working within cloud data environments like Databricks, and querying/manipulating large-scale datasets in data warehouses like BigQuery.
Orchestration & MLOps: Practical experience with machine learning lifecycle and orchestration tools, such as MLflow and Airflow.
Business-Impact Focus: A strong ability to translate raw model results into real-world business outcomes. You know how to balance technical model performance (precision/recall) with financial impact, operational realities, and the user experience.