About this role
We are seeking a highly analytical and detail-oriented Data Scientist to transform raw data into meaningful insights. The role includes building predictive models, performing data analysis, and enabling data-driven decision-making across the organization. You should be comfortable working with large datasets, advanced statistical methods, and modern machine learning frameworks. Key Responsibilities Data Analysis & Modeling Analyze structured and unstructured data to identify trends, patterns, and correlations. Build and deploy machine learning models (classification, regression, clustering, NLP, forecasting). Perform statistical analysis, hypothesis testing, and A/B experiments. Develop dashboards and automated reports for business teams. Data Engineering & Pipeline Development Build and maintain ETL pipelines for data ingestion and transformation. Work with data warehouses and cloud platforms (AWS, GCP, Azure). Ensure data accuracy, cleanliness, and consistency. Business Collaboration Partner with product, engineering, and business teams to translate problems into data solutions. Present insights and recommendations to stakeholders in a clear, actionable manner. Identify opportunities where data can improve efficiency, user experience, or impact. Tools & Technology Write production-level code for models and analytics pipelines. Use version control (Git), containerization (Docker), and model deployment tools. Work with BI tools like Power BI, Tableau, Metabase, or Looker. Qualifications Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Mathematics, or related field. 2–4 years experience in data science, machine learning, or advanced analytics. Strong programming skills in Python (pandas, NumPy, scikit-learn, TensorFlow, PyTorch). Understanding of SQL, relational databases, and data warehousing concepts. Experience with cloud services (AWS S3, Lambda, SageMaker; GCP BigQuery; Azure ML, etc.). Solid grasp of statistics, probability, and mathematical modeling.