Data Scientist

Key Responsibilities

• Data Modeling & Predictive Analytics: Design, train, evaluate, and deploy machine learning algorithms and statistical models (e.g., regression, classification, clustering, time-series forecasting).
• Data Exploration & Analysis: Perform exploratory data analysis (EDA) on large, unstructured, or structured datasets to discover actionable business insights and trends.
• Pipeline & Feature Engineering: Collaborate with Data Engineers to build clean data pipelines, design features, and maintain high standards of data quality and governance.
• Experimentation & A/B Testing: Design, run, and evaluate A/B tests and statistical experiments to assess product changes and strategy optimizations.
• Cross-Functional Collaboration: Partner with product managers and business stakeholders to translate vague business problems into structured analytics projects.
• Visualization & Reporting: Build interactive dashboards and reports using tools like Tableau, Power BI, or Streamlit to effectively communicate model findings to technical and non-technical audiences.

Qualifications & Skills

• Education: Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, Mathematics, Economics, or a related quantitative field.
• Technical Expertise: Strong proficiency in Python or R for data analysis and modeling.
• SQL: Advanced knowledge of SQL for data extraction, querying, and database management (PostgreSQL, Snowflake, BigQuery, etc.).
• Machine Learning: Proficiency with ML frameworks and libraries such as scikit-learn, pandas, NumPy, TensorFlow, PyTorch, or XGBoost.
• Statistics: Solid background in hypothesis testing, statistical distributions, regression techniques, and experimental design.
• Communication: Ability to clearly present complex data findings to business stakeholders.