KANINI - Data Scientist

Job location: [{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Chennai","addressCountry":"IN"}},{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Pune","addressCountry":"IN"}},{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Coimbatore","addressCountry":"IN"}},{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Tamil Nadu","addressCountry":"IN"}}] Work arrangement: Employment type: FULL_TIME Salary: "" Experience requirements: {"monthsOfExperience":96,"educational":{"credentialCategory":"bachelor degree"}} Education requirements: "" Role Overview: We are looking for an experienced Data Scientist with strong expertise in Machine Learning, Python, and statistical modelling to develop, deploy, and scale data-driven solutions. The ideal candidate will have hands-on experience building predictive models, solving complex business problems, and deploying production-ready ML solutions. Key Responsibilities: - Design, develop, and deploy machine learning models for classification, regression, forecasting, and predictive analytics. - Build scalable ML solutions using Python and libraries such as Scikit-learn, TensorFlow, and PyTorch. - Perform data preprocessing, feature engineering, exploratory data analysis, and statistical analysis. - Develop and optimize algorithms to improve model accuracy, performance, and scalability. - Apply supervised, unsupervised, and deep learning techniques to solve business problems. - Deploy and monitor machine learning models in production environments. - Build end-to-end ML pipelines and collaborate with data engineering teams for data integration. - Use model evaluation techniques, validation methods, and performance metrics to ensure reliability. - Collaborate with business and cross-functional teams to translate requirements into actionable data science solutions. - Follow software engineering best practices and Agile delivery methodologies. Mandatory Skills: - 8+ years of experience in Data Science, Machine Learning, or related fields. - Strong hands-on expertise in Python programming. - Excellent understanding of machine learning algorithms, statistical modelling, and predictive analytics. - Experience with Scikit-learn, TensorFlow, PyTorch, or similar ML frameworks. - Strong knowledge of data preprocessing, feature engineering, model evaluation, and optimization. - Experience developing and deploying production-grade machine learning models. - Proficiency in SQL and working with large datasets. - Strong analytical, problem-solving, and communication skills. - Experience working in an Agile environment. Good to Have: - Experience with deep learning, NLP, or Generative AI. - Exposure to cloud platforms such as GCP, AWS, or Azure. - Experience with MLOps, model monitoring, CI/CD, and ML pipeline automation. - Knowledge of big data technologies such as Spark and Databricks. - Experience building scalable enterprise-level ML solutions.