Senior Data Scientist

Job Description: Senior Data Scientist Exp: 5-8 years Notice period: Immediate – 15days Job Location: Chennai Profile source: Tamil Nadu Timings:1:00pm – 10:00pm (IST) Work Mode: WFO (Mon-Fri) We are seeking a strategic and innovative Senior Data Scientist to join our high-performing Data Science team. In this role, you will lead the design, development, and deployment of advanced analytics and machine learning solutions that directly impact business outcomes. You will collaborate cross-functionally with product, engineering, and business teams to translate complex data into actionable insights and data products. Key Responsibilities ● Lead and execute end-to-end data science projects, encompassing problem definition, data exploration, model creation, assessment, and deployment. ● Develop and deploy predictive models, optimization techniques, and statistical analyses to address tangible business needs. ● Articulate complex findings through clear and persuasive storytelling for both technical experts and non-technical stakeholders. ● Spearhead experimentation methodologies, such as A/B testing, to enhance product features and overall business outcomes. ● Partner with data engineering teams to establish dependable and scalable data infrastructure and production-ready models. ● Guide and mentor junior data scientists, while also fostering team best practices and contributing to research endeavors. Required Qualifications & Skills: ● Master’s or PhD in Computer Science, Statistics, Mathematics, or a related discipline. ● 5+ years of practical experience in data science, including deploying models to production. ● Expertise in Python and SQL; ● Solid background in ML frameworks such as scikit-learn, TensorFlow, PyTorch, and XGBoost. ● Competence in data visualization tools like Tableau, Power BI, matplotlib, and Plotly. ● Comprehensive knowledge of statistics, machine learning principles, and experimental design. ● Experience with cloud platforms (AWS, GCP, or Azure) and Git for version control. ● Exposure to MLOps tools and methodologies (e.g., MLflow, Kubeflow, Docker, CI/CD). ● Familiarity with NLP, time series forecasting, or recommendation systems is a plus. ● Knowledge of big data technologies (Spark, Hive, Presto) is desirable.