Data Science Specialist

3 weeks ago

Bengaluru, India Sonata Software Full time

About Sonata Software In today's market, we observe a distinct duality in technology adoption. On one front, clients are keenly focused on cost containment, while on the other, there is a strong drive to modernize their digital storefronts, aiming to appeal to both consumers and B2B customers alike. As a leading Modernization Engineering company, we aim to deliver modernization-driven hypergrowth for our clients based on the deep differentiation we have created in Modernization Engineering, powered by our Lightening suite and 16-step Platformation playbook. In addition, we bring agility and systems thinking to accelerate time to market for our clients.Headquartered in Bengaluru, India, Sonata Software has a strong global presence, with strategic operations spanning across key regions such as the US, UK, Europe, APAC, and ANZ. We are a trusted partner of world-leading companies in TMT (Telecom, Media, and Technology), Retail & CPG, Manufacturing, BFSI (Banking, Financial Services and Insurance), and HLS (Healthcare and Lifesciences). Our bouquet of Modernization Engineering services cuts across Cloud, Data, Dynamics, Contact Centers, and around newer technologies like GeJob Title: Data Science Engineer Experience: 3–8 years Role Summary: Design and deploy scalable data science solutions that drive insights and automation. This role blends data engineering, ML, and MLOps to ensure robust model development and deployment. Key Responsibilities: Data Engineering: Build ETL/ELT pipelines, reusable feature components, and collaborate on data quality. Modeling: Develop ML models (classification, regression, NLP, GenAI), perform EDA, and optimize for production. MLOps: Deploy models using Docker/Kubernetes/cloud (AWS, Azure, GCP), implement CI/CD, and monitor performance. Integration: Expose models via APIs, integrate with BI tools, and maintain drift detection systems. Collaboration: Work with cross-functional teams, contribute to AI/ML CoE, and explore LLMs, RAG, and GenAI. Required Skills: Python (pandas, scikit-learn, TensorFlow, PyTorch) SQL & Data Warehouses (Snowflake, BigQuery, etc.) MLOps tools (MLflow, Kubeflow), cloud platforms Data pipeline tools (Airflow, Databricks) Visualization (Power BI, Tableau) Nice to Have: GenAI, LLM fine-tuning, RAG, vector DBs (FAISS, Pinecone) API development (FastAPI), DevOps tools (Terraform) Certifications (AWS/Azure ML, Microsoft Data Scientist) Education: Bachelor’s/Master’s in CS, Data Science, Stats, or related field Soft Skills: Analytical mindset, clear communication, team player, attention to detail

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