Data Scientist/Engineer

Required Skills & Experience

5+ years of experience in Data Engineering and/or Data Science.

Strong programming skills in Python and SQL for both data engineering and scientific/analytical use cases.

Hands-on experience with Azure data ecosystem (ADLS, Azure Databricks, Synapse, Azure SQL).

Experience with Databricks (Spark, MLflow) for large-scale data processing and model deployment.

Proven experience building data pipelines and working with relational and NoSQL databases.

Strong expertise in machine learning, particularly in time series analysis and/or NLP.

Experience handling complex industrial or oil & gas datasets (or similar large-scale environments).

Ability to work with messy, incomplete, or evolving datasets and deliver meaningful insights.

Nice to Have Skills & Experience

oil and gas experience

Job Description

We are seeking a hybrid Data Engineer / Data Scientist with strong machine learning expertise and hands-on experience building scalable data pipelines and models in Azure environments. This role is ideal for someone comfortable working across the full data lifecycle—from ingestion and engineering to advanced analytics and model deployment—especially within complex industrial or oil & gas datasets.Design, build, and maintain end-to-end data pipelines using Azure services (ADLS, Azure Databricks, Synapse, Azure SQL).

Develop and deploy machine learning models (e.g., time series forecasting, NLP) using Azure ML, Databricks, Spark, and MLflow.

Work with structured, semi-structured, and unstructured data (e.g., sensor data, logs, PDFs, documents), transforming it into actionable insights.

Explore and analyze large datasets, performing data wrangling, EDA, and feature engineering.

Build and optimize complex SQL queries and ensure efficient data processing across relational and NoSQL databases.

Solve open-ended, ambiguous analytical problems, where data quality and requirements may be evolving.

Design experiments, test hypotheses, and iterate quickly in dynamic business environments.

Collaborate with cross-functional teams and communicate insights clearly to non-technical stakeholders.

Contribute to data architecture decisions and act as a technical champion for data engineering and ML best practices.