Senior Staff Data Scientist (Secunderabad)

Senior Staff Data Scientist Experience: 8 to 15 years Location: Hyderabad, India Skills: Python, Pandas, NumPy, Scikit-learn, Supervised Learning, Unsupervised Learning, Regression, Classification, Clustering, Time Series Modeling, Deep Learning, Natural Language Processing (NLP), TensorFlow, PyTorch, LangChain, Llama Index, HuggingFace, BERT, Embeddings, Vector Databases, FAISS, SQL, AWS, GCP, Azure, Azure Open AI, Vertex AI About the Role Solenis GSS India is looking for a highly skilled Senior Data Scientist to join our AI & Data Science team. This role is designed for a strong individual contributor who brings deep expertise in classical Data Science and Machine Learning, with selective and practical exposure to Generative AI. You will work on complex, enterprisescale problems, building and deploying endtoend machine learning and AI solutions that drive measurable business impact across commercial operations, supply chain, and customer experience domains. This role is not focused on reporting, dashboards, or pure analytics. We are seeking handson practitioners who own the full lifecycle of data science solutionsfrom problem definition to production deployment. Key Responsibilities Data Science & Machine Learning Design, build, train, and deploy machine learning models to solve complex business problems. Apply supervised and unsupervised learning techniques including regression, classification, clustering, and timeseries forecasting. Perform advanced exploratory data analysis (EDA), feature engineering, and model validation. Develop and optimize deep learning and NLP models using frameworks such as TensorFlow and PyTorch. Generative AI & Advanced AI Apply Generative AI techniques as an extension of core Data Science solutions. Build LLMpowered workflows using frameworks such as LangChain and HuggingFace. Work with embeddings, vector databases, and tools such as FAISS for intelligent search and retrieval use cases. Collaborate on AI agentbased solutions using tools like CrewAI or AutoGen where applicable. Ensure GenAI solutions are productionready, scalable, and aligned with business needs. Enterprise Data & Production Systems Work with structured and unstructured data across enterprise platforms. Collaborate with data engineering teams to ensure robust and scalable data pipelines. Develop solutions deployed on cloud platforms such as AWS, GCP, or Azure. Contribute to model deployment, monitoring, and optimization in production environments. Collaboration & Communication Partner with product managers, engineers, and business stakeholders to translate business problems into data science solutions. Communicate insights, model outcomes, and recommendations clearly to both technical and nontechnical audiences. Provide technical guidance and mentorship to junior team members when required. Required Skills & Qualifications Education Bachelors degree in Computer Science, Data Science, Statistics, Engineering, or a related field. Masters degree such as M.Tech / PGP / PGD in Data Science or Business Analytics preferred. Coursework completed 2017 or earlier is strongly preferred. Core Technical Skills Strong proficiency in Python Handson experience with: Pandas, NumPy Scikitlearn Supervised & Unsupervised Learning Regression, Classification, Clustering Time Series Modeling Deep Learning Natural Language Processing (NLP) Experience with TensorFlow and/or PyTorch Advanced / GenAI Skills Practical experience with: LangChain, Llama Index HuggingFace, BERT Embeddings & Vector Databases (e.g., FAISS) SQL Exposure to: AWS, GCP, or Azure Azure Open AI / Vertex AI Experience Requirements 815 years of overall experience with robust handson Data Science and ML ownership. Proven track record of delivering endtoend ML solutions in enterprise environments. Experience working as an Individual Contributor on complex technical problems. Preferred Experience Exposure to industrial, manufacturing, or enterprise commercial domains. Experience working with largescale enterprise data platforms. Familiarity with responsible AI practices, model governance, and data quality frameworks. .