Data Scientist / AI Engineer – Energy & Asset Intelligence

Please apply directly or send your resume to request@tytantech.com with the subject line : “Application – Data Scientist / AI Engineer – Energy & Asset Intelligence”

About the Role

We are seeking a highly skilled Data Scientist / AI Engineer to join our growing AI & Analytics Team within a leading Oil & Gas Private Equity firm. This role is ideal for individuals who combine strong data science and software engineering expertise with a deep understanding of energy markets, operations, or asset optimization . You’ll work closely with our investment, engineering, and technology teams to develop advanced models and data-driven products that enhance decision-making across exploration, production, asset management, and capital allocation.

Key Responsibilities

Design and implement AI and ML models that support investment analysis, production forecasting, equipment optimization, and market intelligence.

Build end-to-end data pipelines for data ingestion, transformation, and feature engineering using structured and unstructured datasets (production logs, financial data, IoT, etc.).

Develop and deploy machine learning solutions (predictive, prescriptive, generative) leveraging modern frameworks (PyTorch, TensorFlow, Scikit-learn, LangChain, etc.).

Collaborate with engineers, investment analysts, and domain experts to translate business challenges into analytical solutions .

Work on GenAI and NLP-based applications for unstructured document analysis, deal evaluation, and portfolio reporting.

Write production-grade, scalable code for ML pipelines and APIs (Python, FastAPI, Flask, etc.).

Conduct exploratory data analysis (EDA) , model validation, and performance tracking using statistical and visualization techniques.

Contribute to cloud-based ML workflows (Azure, AWS, or GCP), including containerization (Docker) and orchestration (Kubernetes).

Support internal innovation projects on Agentic AI, MLOps, and generative intelligence for operational insights .

Qualifications

Bachelor’s or Master’s degree in Computer Science, Data Science, Petroleum Engineering, Applied Mathematics, or related fields .

3–7 years of hands-on experience in data science, AI engineering, or ML operations, preferably in Oil & Gas, Energy, or Financial Services sectors.

Proven ability to design and implement ML algorithms , time-series models, and optimization techniques.

Strong programming skills in Python (Pandas, NumPy, Scikit-learn, PyTorch / TensorFlow).

Experience with data querying and processing tools (SQL, PySpark, Databricks, or Snowflake).

Knowledge of GenAI / LangChain / OpenAI API for automation or analytical applications.

Familiarity with cloud platforms (Azure, AWS, or GCP) and CI / CD pipelines for ML deployment.

Excellent analytical, problem-solving, and communication skills, with the ability to work cross-functionally in technical and investment teams.

Preferred Experience (Nice-to-Have)

Exposure to energy trading analytics , asset performance modeling , or reservoir data analysis .

Experience in developing AI copilots or internal chatbots using enterprise data.

Understanding of financial modeling or risk analysis in private equity or investment environments.

Experience integrating LLMs or knowledge graphs for document intelligence or portfolio insights.