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 : 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.