Data science

About Sonata SoftwareIn 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: www.sonata-software.comJob Title: Data Science Engineer Experience: 3–8 years Role Overview: The Data Science Engineer will be responsible for building scalable data science solutions that drive actionable insights and automation across business functions. The role bridges data engineering, machine learning, and software engineering, ensuring models are efficiently trained, deployed, and maintained in production. The ideal candidate combines strong data science fundamentals with practical implementation skills in MLOps and cloud 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 reusable feature engineering components and integrate them into ML workflows. 2. Model Development & Evaluation Develop, train, and validate machine learning and statistical models using Python and modern ML frameworks (scikit-learn, TensorFlow, PyTorch). Perform exploratory data analysis (EDA) to identify trends, correlations, and patterns. Apply techniques in classification, regression, clustering, forecasting, NLP, or Generative 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 using Docker, Kubernetes, or cloud-native services (AWS Sagemaker, Azure ML, GCP Vertex AI). Implement CI/CD pipelines for model retraining and deployment. Collaborate with MLOps teams to ensure scalable, secure, and reliable ML operations. 4. Data Science Platform Integration Work with data engineers and BI teams to integrate ML insights into business dashboards and decision systems. Leverage APIs and microservices to expose model outputs to applications. Develop and maintain model monitoring and drift detection systems. 5. Collaboration & Innovation Partner with data analysts, domain experts, and product teams to translate business problems into data science solutions. Contribute to the AI/ML Center of Excellence (CoE) by documenting reusable assets, best practices, and frameworks. Explore and prototype emerging AI/ML techniques, including LLM, RAG, and GenAI use cases. Required Skills & Experience: 3–8 years of experience in data science, machine learning, or AI engineering roles. Proficiency in Python (pandas, numpy, scikit-learn, TensorFlow, PyTorch). Strong SQL skills and experience with data warehouses (Snowflake, BigQuery, Synapse, Redshift). Familiarity with data pipelines using Airflow, Databricks, or Azure Data Factory. Practical knowledge of MLOps tools such as MLflow, Kubeflow, or Vertex AI. Hands-on experience with cloud platforms (AWS, Azure, or GCP). Understanding of model evaluation, tuning, and versioning practices. Experience in data visualization and storytelling using Power BI, Tableau, or Plotly. Good-to-Have Skills: Exposure to Generative AI, LLM fine-tuning, or prompt engineering. Experience in RAG architecture, vector databases (Pinecone, FAISS, Chroma). Knowledge of big data technologies (Spark, Hadoop). Experience with API development (FastAPI, Flask). Familiarity with DevOps / GitOps practices 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.