AI/ML Data Scientist

Accountabilities:: Design, train, evaluate, and optimize machine learning and deep learning models across use cases including classification, regression, clustering, predictive analytics, and time-series forecasting. Extract, clean, transform, and engineer high-value predictive features from large-scale structured and unstructured datasets to improve model performance and business outcomes. Build robust end-to-end data ingestion and model training pipelines, incorporating MLOps best practices and automated retraining processes to support scalable machine learning operations. Conduct rigorous model validation and optimization through hyperparameter tuning, cross-validation, bias-variance analysis, and other evaluation techniques to ensure production models remain accurate and stable. Analyze complex datasets and statistical outputs to identify meaningful patterns, trends, and opportunities that can inform strategic and operational decision-making. Translate technical findings, predictive metrics, and statistical insights into clear dashboards, reports, and actionable recommendations for business stakeholders. Collaborate with software engineering teams to integrate statistical and machine learning models into customer-facing software products and production environments. Requirements: Bachelor’s or Master’s degree in Statistics, Mathematics, Computer Science, Data Science, or another quantitative discipline, combined with approximately 3–7 years of relevant professional experience. Advanced Python programming skills with strong hands-on experience using data science and machine learning libraries such as Pandas, NumPy, Scikit-Learn, PyTorch, and/or TensorFlow. Strong knowledge of statistical concepts and methodologies, including probability, statistical testing, hypothesis validation, predictive modeling, and time-series analysis. Practical experience developing machine learning and deep learning models, including model evaluation, feature engineering, hyperparameter optimization, and validation techniques. Experience building or contributing to automated data and machine learning pipelines, with an understanding of MLOps principles and production-oriented model development. Working knowledge of SQL databases, Git version control, and cloud-based data platforms such as Snowflake, BigQuery, or AWS Redshift. Strong analytical and problem-solving capabilities, with the ability to work with complex datasets and communicate technical findings clearly to both technical and business stakeholders. Benefits: Annual compensation ranging from INR 2,400,000 to INR 3,800,000 CTC, depending on relevant experience and qualifications. Full-time employment opportunity within a mid-senior-level AI and data science environment. Remote work flexibility across India, with opportunities to work from Bangalore or Pune under a hybrid or remote-flexible arrangement. Opportunity to work on predictive analytics, machine learning, deep learning, forecasting, and MLOps initiatives with direct impact on enterprise decision-making. Exposure to large-scale datasets, modern data platforms, and customer-facing software products while collaborating closely with software engineering teams.