Senior Data Scientist

Incedo Inc.; Gurugram District; On-site; INR 2,000,000 - 4,000,000; Full time

Job summary

Incedo Inc. in Gurugram invites a Senior Data Scientist / ML Engineer to design, develop, and deploy machine learning models at scale.

The role focuses on bridging data science with software engineering to build reliable, production-ready pipelines and automation. You will implement end-to-end ML workflows, integrate models with enterprise systems, and apply MLOps practices for versioning, testing, deployment, and retraining.

Qualifications

• Hands-on experience in machine learning model development, evaluation, and optimization.
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Responsibilities

• Develop, train, evaluate, and optimize machine learning models for classification, regression, forecasting, and other business use cases.

Skills

Python

SQL

OO programming

Software engineering

Problem solving

Education

Bachelor's or Master’s degree in CS/DS/Engineering

Tools

Airflow

MLflow

Spark/PySpark

Kubernetes

Cloud ML services

Feature stores

Job description

Role: Senior Data Scientist / ML Engineer

Location: Gurugram

Employment type: Full Time

Experience: 3 to 8 years

Role Summary

We are seeking a Data Scientist / ML Engineer with hands‑on expertise in developing machine learning models and engineering scalable, automated solutions for executing them in production. The candidate should bridge data science and software engineering, translating models into reliable, reusable, and production‑ready pipelines with a strong focus on automation, performance, and scalability.

Key Responsibilities

• Develop, train, evaluate, and optimize machine learning models for classification, regression, forecasting, and other business use cases.
• Implement end‑to‑end ML workflows covering data ingestion, preprocessing, feature engineering, model training, evaluation, and inference.
• Build automated, reusable, and scalable pipelines for model execution, batch scoring, and real‑time inference, as applicable.
• Develop production‑quality Python code using libraries such as pandas, NumPy, scikit‑learn, XGBoost, and TensorFlow/PyTorch, as relevant.
• Integrate models with enterprise applications, APIs, databases, and data platforms in collaboration with engineering teams.
• Implement MLOps practices, including model versioning, experiment tracking, CI/CD, automated testing, deployment, and rollback.
• Monitor production models for performance degradation, data drift, model drift, latency, and reliability; automate alerts and retraining workflows where appropriate.
• Optimize processing performance, resource utilization, execution time, and infrastructure costs for large‑scale workloads.
• Work with data engineering, DevOps, and cloud teams to operationalize models using platforms such as Azure ML, AWS SageMaker, GCP, or Databricks.
• Ensure code quality, reproducibility, logging, error handling, documentation, and adherence to security and governance standards.

Required Skills & Expertise

• Strong proficiency in Python, object‑oriented programming, SQL, and software engineering best practices.
• Hands‑on experience in machine learning model development, evaluation, and optimization.
• Expertise in building automated data processing and ML pipelines using tools such as Airflow, MLflow, or equivalent technologies.
• Knowledge of model monitoring, retraining, scalability, distributed processing, and performance optimization.
• Strong troubleshooting, analytical thinking, ownership, communication, and proactive problem‑solving skills.

Preferred Qualifications

• Experience in banking, lending, credit risk, fraud detection, or other financial services use cases.
• Exposure to Spark/PySpark, Kubernetes, cloud‑native ML services, feature stores, and real‑time streaming.
• Familiarity with model governance, explainability, security, and responsible AI practices.
• Bachelor's or master's degree in Computer Science, Data Science, Engineering, Mathematics, or a related discipline.