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