Role Overview We are looking for a Data Scientist with a strong foundation in Data Engineering who can bridge scalable data systems with advanced machine learning solutions. This role is ideal for professionals who started their journey building robust data pipelines and evolved into deploying impactful ML models.
You will work closely with engineering, product, and analytics teams to design end-to-end data solutions — from ingestion to model deployment.
Key Responsibilities
- Design and build scalable data pipelines (batch & real-time)
- Perform advanced feature engineering on large structured & unstructured datasets
- Develop, train, validate, and deploy machine learning models
- Optimize data workflows using Spark / Airflow / distributed systems
- Collaborate with data engineers to improve data quality and architecture
- Deploy ML models into production environments
- Monitor model performance and continuously improve accuracy
- Work with cloud platforms (AWS / GCP / Azure)
Required Skills Robust Data Engineering Background
- Experience building ETL/ELT pipelines
- Expertise in SQL and Python
- Hands-on with Spark / Hadoop ecosystem
- Experience with Airflow or orchestration tools
- Data modeling & warehouse concepts
Data Science & ML
- Strong understanding of statistics & ML algorithms
- Experience in model building (regression, classification, clustering)
- Feature engineering expertise
- Model evaluation & optimization
- ML deployment experience
Cloud & Tools
- AWS / GCP / Azure experience
- Experience with ML deployment frameworks
- Familiarity with Docker / CI-CD is a plus
Ideal Candidate
- 5–10 years total experience
- Started as Data Engineer and transitioned into Data Science
- Strong problem-solving mindset
- Experience working in product-based organizations preferred
- Ability to own projects end-to-end
Good to Have
- Experience with real-time streaming systems (Kafka)
- Exposure to MLOps practices
- Experience in large-scale distributed data systems
📌 Senior Data Scientist (Hyderabad)
🏢 ICE
📍 Hyderabad
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