Drive end-to-end machine learning projects - from problem formulation and data exploration to model development, evaluation, and deployment.
Build scalable, production-ready feature pipelines and models for use cases like personalization, recommendations and ranking.
Collaborate with ML engineers, product managers, and analysts to define success metrics, align on priorities, and drive impact through data science solutions.
Contribute to best practices in model development, experiment design, and model monitoring
Stay current with advances in machine learning and actively contribute ideas for innovation.

Requirements

2+ years of experience in applying data science or machine learning to real-world problems in a production environment
Strong proficiency in Python and SQL for data analysis and model development.
Hands-on experience with ML libraries/frameworks like Scikit-learn, PyTorch, or TensorFlow.
Solid understanding of statistics, probability, and A/B testing concepts.
Ability to translate ambiguous business problems into well-defined, actionable data science objectives
Innovative problem solver with eagerness to learn, strong analytical thinking, and ability to clearly communicate complex technical ideas.
Experience with personalization, recommendation systems and other Machine learning algorithms.
Exposure to working with big data tools like Spark and cloud services (e.g., AWS, GCP).
Ability to quickly understand, debug and solve model post production issues
Familiarity and Hands on experience with LLMs or GenAI concepts like RAG, PEFT.

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

machine learningdata analysismodel developmentPythonSQLScikit-learnPyTorchTensorFlowstatisticsA/B testing

Soft Skills

analytical thinkingproblem solvingcommunicationcollaborationinnovationadaptabilityprioritizationcreativitycritical thinkingdata-driven decision making