Data Scientist

We are looking for a Data Scientist to turn complex data into actionable insights and production-ready models that drive business decisions. You will work with cross-functional teams to identify opportunities, design experiments, build predictive models, and communicate findings to technical and non-technical stakeholders. The ideal candidate combines strong statistical foundations with hands‑on ML engineering and business acumen.

• Collect, clean, and analyze structured/unstructured data from multiple sources.
• Build, evaluate, and deploy machine learning models for classification, regression, forecasting, and ranking problems.
• Perform exploratory data analysis (EDA) to identify patterns, anomalies, and opportunities.
• Design and run A/B tests and other experiments; measure impact and provide recommendations.
• Develop feature engineering pipelines and maintain reusable datasets.
• Partner with data engineers to productionize models and monitor performance/drift.
• Create dashboards/reports and present insights to product, engineering, and leadership teams.
• Document methodologies, assumptions, and model limitations.
• Ensure data quality, governance, privacy, and compliance best practices.
• Continuously research and apply state-of-the-art techniques where relevant.

• Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Mathematics, Economics, or related field.
• 3+ years of experience in data science, machine learning, or advanced analytics.
• Strong proficiency in Python and SQL.
• Solid understanding of statistics, probability, hypothesis testing, and experimental design.
• Hands‑on experience with ML libraries (e.g., scikit-learn, XGBoost, TensorFlow, or PyTorch).
• Experience building end‑to‑end analytical solutions from problem framing to deployment.
• Strong communication skills with ability to explain complex findings clearly.

Preferred Qualifications

• Experience with NLP, recommendation systems, or time-series forecasting.
• Familiarity with cloud platforms (AWS/GCP/Azure) and MLOps tools.
• Experience with big data tools (Spark, Databricks, or similar).
• Exposure to feature stores, model registries, and model monitoring frameworks.
• Domain experience in HR Tech, SaaS, FinTech, Healthcare, or E-commerce.