Data Sc

Key Responsibilities

• Problem framing: partner with product and business stakeholders to translate ambiguous business questions into well-defined, testable data science problems.
• Analysis: conduct exploratory data analysis, hypothesis testing, and statistical analysis to generate and validate insights that inform product/business decisions.
• Model development: design, build, train, and validate machine learning models classification, regression, clustering, time series, or NLP depending on team focus to solve defined business problems.
• Experimentation: design and analyze experiments (A/B tests, causal inference methods) to measure the real-world impact of product and model changes.
• Validation rigor: rigorously validate model performance using appropriate metrics, back-testing, and out-of-sample validation before recommending production deployment.
• Communication: communicate findings, model limitations, and recommendations clearly to both technical and non-technical stakeholders, including executive audiences.
• Production hand-off: partner with AI/ML Engineers and Data Engineers to productionize validated models, defining the handoff contract features, retraining cadence, monitoring requirements.
• Ongoing monitoring: monitor deployed model performance over time and lead root-cause analysis when models degrade or behave unexpectedly.
• Mentorship: mentor junior data scientists/analysts on statistical rigor, experiment design, and modeling best practices.

Required Qualifications

• 5+ years of experience in data science, applied statistics, or machine learning roles, with a track record of models/analyses deployed to production or used for real decision-making.
• Strong foundation in statistics hypothesis testing, experiment design, causal inference and classical/modern ML techniques.
• Proficiency in Python or R, SQL, and standard data science libraries (pandas, scikit-learn, statsmodels).
• Experience designing and analyzing A/B tests or other experimental/quasi-experimental methods.
• Strong communication skills able to explain statistical/modeling concepts and their limitations clearly to non-technical stakeholders.
• Experience partnering with engineering teams to operationalize models into production systems.

Preferred Qualifications

• Experience with deep learning/NLP techniques relevant to the team's domain (e.g., transformer-based models, time-series forecasting, graph-based methods).
• Domain expertise in the team's specific industry.
• Experience with MLOps concepts (model versioning, monitoring, drift detection) even if not directly implementing them.
• Advanced degree (MS/PhD) in statistics, applied math, computer science, or a related quantitative field.

Tools & Technical Stack

Python/R, SQL, Jupyter, scikit-learn, statsmodels/PyMC, pandas/numpy, in-house or third-party experimentation platforms, BI tools (Looker/Tableau) for communicating findings, MLflow/Weights & Biases for experiment tracking.