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: www.sonata-software.com

Job Title: Data Science Engineer

Experience: 3–8 years

Role Overview:

Data Science Engineer

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.