Data Scientist - Machine Learning - Predictive Analytics (5-10 yrs)

Job location: Mumbai, Gurugram, Bengaluru Overall experience: 5 to 10 years Employment type: Full Time, Permanent Job type: fulltime Role Overview : The role involves working with large and diverse datasets, developing robust analytical and machine learning models, generating actionable insights, and collaborating closely with product, engineering, and business teams. Key Responsibilities : Data Science & Machine Learning : - Develop, train, validate, and deploy machine learning models to solve complex business and product problems. - Apply supervised and unsupervised learning techniques to structured and unstructured datasets. - Build predictive models for classification, regression, forecasting, recommendation, segmentation, and anomaly detection use cases. - Select appropriate algorithms based on business objectives, data characteristics, and model performance requirements. - Continuously improve model accuracy, scalability, robustness, and interpretability. Statistical Analysis & Modelling : - Apply statistical techniques to identify patterns, relationships, trends, and anomalies within large datasets. - Perform hypothesis testing, statistical inference, correlation analysis, and experimentation. - Design and analyse A/B tests and controlled experiments where required. - Develop statistical and mathematical models to support business and product decisions. - Evaluate model assumptions and ensure statistical validity of analytical outcomes. Data Preparation & Feature Engineering : - Work with data engineering teams to identify and prepare relevant data sources. - Perform data cleaning, preprocessing, transformation, and exploratory data analysis. - Develop meaningful features from raw data to improve model performance. - Handle missing data, outliers, data inconsistencies, and other data-quality challenges. - Build reusable data preparation and feature engineering pipelines. Predictive Analytics : - Develop predictive models to forecast business outcomes and identify future trends. - Analyse customer, product, operational, and behavioural data to generate actionable insights. - Identify opportunities for optimization through predictive and prescriptive analytics. - Translate analytical findings into recommendations that can influence business and product strategy. Model Evaluation & Optimization : - Establish appropriate metrics to evaluate machine learning model performance. - Perform model validation, cross-validation, error analysis, and performance benchmarking. - Tune model hyperparameters and optimize algorithms for improved performance. - Monitor model behaviour and identify model drift or degradation after deployment. - Ensure models balance accuracy, interpretability, scalability, and business requirements. Productionization & Deployment : - Collaborate with data engineers and software engineers to productionize machine learning models. - Develop production-ready data science solutions using Python and relevant ML frameworks. - Create scalable model pipelines and APIs for integrating ML models into applications. - Support model deployment, monitoring, testing, and lifecycle management. - Follow engineering best practices around version control, testing, documentation, and reproducibility. Business & Product Analytics : - Partner with Product, Engineering, Business, and Operations teams to understand business problems and translate them into analytical solutions. - Convert complex analytical findings into clear business recommendations. - Present insights and model outcomes to technical and non-technical stakeholders. - Identify new opportunities where data science and machine learning can create measurable business impact. Research & Innovation : - Stay current with developments in machine learning, statistical modelling, and data science. - Evaluate new algorithms, frameworks, and modelling techniques. - Conduct proof-of-concepts and experiments to assess new approaches. - Contribute to the development of reusable data science methodologies and best practices. Required Skills : - 5 - 10 years of professional experience in Data Science, Machine Learning, Predictive Analytics, or a related field. - Strong programming skills in Python. - Strong understanding of Machine Learning algorithms and statistical modelling. - Experience with supervised and unsupervised learning techniques. - Strong knowledge of feature engineering, model evaluation, validation, and optimization. - Hands-on experience with libraries/frameworks such as Scikit-learn, Pandas, NumPy, and relevant ML frameworks. - Strong understanding of statistical concepts including hypothesis testing, probability, distributions, regression, and experimental design. - Experience working with large datasets and complex data problems. - Strong SQL skills for data extraction, transformation, and analysis. - Experience taking machine learning models from development to production. - Strong analytical and problem-solving capabilities. - Excellent communication and stakeholder management skills. Good to Have : - Experience with Deep Learning and frameworks such as PyTorch or TensorFlow. - Experience with cloud platforms such as AWS, GCP, or Azure. - Exposure to distributed data processing technologies such as Spark. - Experience with recommendation systems, forecasting, NLP, fraud detection, or anomaly detection. - Knowledge of MLOps, model monitoring, CI/CD, and ML model lifecycle management. - Experience working in a product-based technology environment. - Experience solving large-scale business and customer analytics problems. Qualifications : - Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related quantitative discipline. - Strong hands-on experience applying data science to real-world business problems.