Assistant Manager - Data Scientist

Assistant Manager Data Scientist We are looking for an experienced Data Scientist to join our team and drive data-driven decision-making across the organization. The ideal candidate will have a strong foundation in statistical analysis, machine learning, and business problem-solving, with proven experience translating data into actionable insights. Key Responsibilities Design, build, and deploy machine learning models to solve business problems (classification, regression, clustering, recommendation systems, etc.) Perform exploratory data analysis (EDA) to identify trends, patterns, and anomalies in large datasets Collaborate with product, engineering, and business teams to define data science use cases and success metrics Develop and maintain data pipelines in partnership with data engineering teams Conduct A/B testing and statistical experiments to validate hypotheses and measure impact Communicate findings and recommendations to both technical and non-technical stakeholders through reports, dashboards, and presentations Own end-to-end model lifecycle: from data collection and feature engineering to model deployment and monitoring Stay current with the latest research and best practices in data science and machine learning Mentor junior data scientists/analysts as needed Required Skills & Qualifications Bachelor's/Master's degree in Computer Science, Statistics, Mathematics, Data Science, or a related field 8+ years of hands-on experience in data science, applied machine learning, or a similar analytical role Strong proficiency in Python (Pandas, NumPy, Scikit-learn) and/or R Solid understanding of statistics, probability, and experimental design Experience with SQL and working with relational/non-relational databases Hands-on experience with ML frameworks such as Scikit-learn, XGBoost, TensorFlow, or PyTorch Experience with data visualization tools (Tableau, Power BI, or Matplotlib/Seaborn) Familiarity with cloud platforms (AWS, GCP, or Azure) for model deployment Strong problem-solving skills and ability to work with ambiguous business problems Excellent communication skills to present technical findings to non-technical audiences Experience with MLOps tools (MLflow, Airflow, Docker, Kubernetes) Exposure to NLP, computer vision, or time-series forecasting Experience working in Agile/Scrum environments Knowledge of big data tools (Spark, Hadoop) .