Data science

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 Generative AI, MS Fabric, and other modernization platforms. To know more, visit:Job Title: Data Science Engineer Experience: 3–8 yearsRole Overview: TheData Science Engineerwill be responsible for building scalable data science solutions that drive actionable insights and automation across business functions. The role bridgesdata engineering,machine learning, andsoftware engineering, ensuring models are efficiently trained, deployed, and maintained in production. The ideal candidate combines strong data science fundamentals with practical implementation skills inMLOpsandcloud environments.Key Responsibilities: 1. Data Preparation & Feature Engineering Design and implement data pipelines for data extraction, transformation, and loading (ETL/ELT). Collaborate with data engineers to ensure high-quality, well-structured, and accessible datasets. Build reusablefeature engineeringcomponents and integrate them into ML workflows. 2. Model Development & Evaluation Develop, train, and validatemachine learningandstatistical modelsusing Python and modern ML frameworks (scikit-learn, TensorFlow, PyTorch). Performexploratory data analysis (EDA)to identify trends, correlations, and patterns. Apply techniques inclassification, regression, clustering, forecasting, NLP, orGenerative AI, depending on the use case. Evaluate models using appropriate metrics and optimize performance for production-readiness. 3. Model Deployment & MLOps Package and deploy ML models to production environments usingDocker,Kubernetes, orcloud-native services(AWS Sagemaker, Azure ML, GCP Vertex AI). ImplementCI/CD pipelinesfor model retraining and deployment. Collaborate with MLOps teams to ensure scalable, secure, and reliable ML operations. 4. Data Science Platform Integration Work withdata engineersandBI teamsto integrate ML insights into business dashboards and decision systems. LeverageAPIsandmicroservicesto expose model outputs to applications. Develop and maintainmodel monitoring and drift detectionsystems. 5. Collaboration & Innovation Partner withdata analysts,domain experts, andproduct teamsto translate business problems into data science solutions. Contribute to theAI/ML Center of Excellence (CoE)by documenting reusable assets, best practices, and frameworks. Explore and prototype emerging AI/ML techniques, includingLLM,RAG, andGenAI use cases.Required Skills & Experience: 3–8 years of experience indata science,machine learning, orAI engineeringroles. Proficiency inPython(pandas, numpy, scikit-learn, TensorFlow, PyTorch). Strong SQL skills and experience withdata warehouses(Snowflake, BigQuery, Synapse, Redshift). Familiarity withdata pipelinesusingAirflow,Databricks, orAzure Data Factory. Practical knowledge ofMLOpstools such asMLflow,Kubeflow, orVertex AI. Hands-on experience withcloud platforms(AWS, Azure, or GCP). Understanding ofmodel evaluation, tuning, and versioningpractices. Experience indata visualization and storytellingusing Power BI, Tableau, or Plotly.Good-to-Have Skills: Exposure toGenerative AI,LLM fine-tuning, orprompt engineering. Experience inRAG architecture,vector databases(Pinecone, FAISS, Chroma). Knowledge ofbig data technologies(Spark, Hadoop). Experience withAPI development(FastAPI, Flask). Familiarity withDevOps / GitOpspractices and infrastructure-as-code tools (Terraform). Certification:Microsoft Certified: Data Scientist Associate,AWS Certified Machine Learning – Specialty, or equivalent.Education: Bachelor’s or Master’s in Computer Science, Data Science, Statistics, Mathematics, or related discipline.Soft Skills: Strong analytical and problem-solving mindset. Ability to communicate complex concepts to non-technical audiences. Team-oriented, with a focus on innovation and continuous learning. Attention to detail and data quality.