Data Scientist Lead L1

Job description:

Job Description

Years of Experience: 5-10years,

Work timing - 01:30 -10:30 PM shifts

Preferable: Production Engineering knowledge Oil & Gas industry

Timeseries ML and constrained optimization for production ramp/sequence planning; ability to encode facility guardrails and drawdown targets.

Pressure system feature engineering using DHP/WHP/Manifold/Up/Downstream signals; comfort reconciling telemetry with physical intuition. 

OT/historian (PI) data wrangling at scale; robust handling of gaps, sensor drift, and event slicing for ramp windows. 

Azure ML model packaging, endpoints, monitoring; handson with CI/CD for retrains and can migrate from HPCtrained models to cloudserved artifacts.

Operatorcentric delivery: translating model outputs into clear ramp steps/visual cues (stoplights, countdowns) and validating against controlroom practice.

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Profile Requirement :

A highly skilled Machine Learning Engineer with 5–10 years of experience in timeseries forecasting, sensorlevel feature engineering, and optimization models for industrial/energy systems. Strong background working with PI historian, operational telemetry, and production facility constraints. Proficient in designing endtoend ML pipelines—from OT data extraction and feature engineering to model deployment, monitoring, and operatorcentric UI delivery. Adept at translating complex ML/optimization outputs into clear operational instructions used by field/production teams.

Senior ML Engineer (6+ years)

Applied Scientist – Energy Optimization

OT Data + ML Specialist

Azure ML: Pipelines, endpoints, environments, registries

ADF and Azure Databricks (PySpark, Delta Lake, DLT)

CI/CD via Azure DevOps — YAML pipelines, automated retrains

Monitoring: Application Insights, Azure Monitor, data drift monitors

Containerization (Docker), ONNX model packaging

Working knowledge of containerization (Docker) and API deployment (FastAPI/Flask).

🔹 Preferred Qualifications

Experience in oil & gas, energy, or industrial automation environments.

Exposure to artificial lift systems (ESP/gaslift) or hydraulic flow models.

Knowledge of physicsbased modeling, surrogate modeling, or hybrid ML+physics workflows.

Experience with realtime streaming (Event Hubs, Kafka, IoT Hub).

 

 

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Deliver 

 No. Performance Parameter Measure 1. Demand generation Order booking 2. Revenue generation through delivery Timeliness, customer success stories, customer use cases 3. Capability Building & Team Management % trained on new skills, Team attrition %

 

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Mandatory Skills: Data Science .

 

Experience: 5-8 Years
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