Data Scientist III :

- Gurgaon, India; Hyderabad, India
- Information Technology
- 328085

About the Role : Grade Level (for internal use) : 10 The Role : Data Scientist III

The Team : The Impact : This role is critical because it turns the organization’s unified data into scalable, production-grade data products—enabling accurate forecasting, early anomaly detection, and smarter risk decisions that directly improve operational efficiency and financial outcomes.

By combining modern lakehouse architecture with AI/ML, it empowers internal teams with self-service insights while delivering faster, more reliable, and data-driven solutions to clients—enhancing trust, competitiveness, and market responsiveness.

What’s in it for you :

- Opportunity to design and own a modern, end-to-end Databricks lakehouse platform, working at the intersection of data engineering, architecture, and applied AI/ML.
- High visibility role with exposure to senior stakeholders, business leaders, and decision-makers, directly influencing strategy through data-driven insights.
- Strong growth environment with hands-on experience in cutting-edge technologies and continuous learning through collaboration and mentorship.
- Chance to work on enterprise-wide, high-impact data products with a global scope, enabling innovation across diverse business domains and markets.

Years of Experience and Qualification : Typically, you’ll bring 7+ years of experience in data engineering, Machine Learning and building production-grade data pipelines and platforms in a cloud environment. A Bachelor’s degree in Computer Science, Engineering, Statistics, Mathematics, or a related field is required; a Master’s degree is a plus.

Preferred qualifications include experience with Databricks/Spark and lakehouse architectures,



along with relevant cloud certifications (e.g., Azure/AWS/GCP data engineering) or Databricks certifications.

Responsibilities : Data Platform & Architecture

- Architect data lake/lakehouse platforms for forecasting, anomaly detection, and BI/analytics using Databricks, Spark, and Delta-style patterns.
- Set and enforce engineering standards for data modeling, integration, and pipeline design across teams/products.
- Lead cloud event-driven/microservices architecture that integrates with web front ends and APIs.

ELT/ETL & Pipeline Engineering

- Design, build, and optimize batch and event-driven ELT/ETL pipelines for analytical and operational workloads.
- Build ingestion/transformation flows aligned to bronze/silver/gold, with validation, CI/CD, testing, and observability baked in.

Advanced Analytics, Forecasting & Anomaly Detection

- Implement scalable time-series forecasting training and inference across categories.
- Build model monitoring (RMSE/MAE/bias/coverage/residuals) with dashboards and automated alerts.
- Develop residual/outlier detection using Z-scores, PELT change points, and confidence-interval breach checks.
- Implement classification/risk-scoring models (e.g., Logistic Regression, Random Forest, XGBoost, clustering, HMMs) for anomaly classification and category risk.

Data Quality, Schema & Drift Monitoring

- Automate data quality and schema validation (missing dates/targets, type changes,



schema evolution, allocation shifts).
- Detect drift in data and model behavior (e.g., allocation stability, category mix changes).
- Deliver per-category anomaly flags and summarized insights for decision-makers.

Leadership & Collaboration

- Provide technical leadership (code/design reviews, mentoring, knowledge sharing) for engineers and data scientists.
- Collaborate with product/UX/business to refine requirements, prioritize work, and plan roadmaps.
- Champion engineering excellence: quality, performance, and operational readiness.

What We’re Looking For : Core Data & Engineering

- Expert SQL (complex joins, window functions, performance tuning at scale).
- Strong Python for data processing, APIs/microservices, and analytics.
- Hands-on PySpark for large-scale distributed processing and ETL/ELT.
- Solid data modeling (dimensional models, star/snowflake schemas, medallion design).

Data Platforms & Cloud

- Proven Databricks experience (notebooks, jobs, clusters, Delta tables) and lakehouse architectures.
- Robust grasp of cloud-native, event-driven architectures (queues, event buses, serverless triggers) for data/ML workflows.
- Experience designing/operating microservices exposing REST/gRPC APIs for forecasting and analytics.

Statistical Modeling & ML

- Hands-on, production experience with time-series forecasting (ARIMA or similar).
- Applied anomaly detection/classification: residual analysis, Z-score, PELT, Logistic Regression, Random Forest, XGBoost, clustering, HMMs.
- Familiarity with end-to-end predictive analytics, including model validation and monitoring.

Monitoring, Quality & MLOps

- Experience building monitoring for models/pipelines (metrics, dash

📌 Data Scientist Iii (Gurugram)
🏢 sep global
📍 Gurugram