Lead / Sr Analyst Data Scientist
About The Position
The Data Scientist plays a key role in advancing digital innovation by developing data-driven models and insights that support operational efficiency, asset optimization, and strategic decision-making. This role will work closely with data engineers, business stakeholders, and digital leadership to design and deploy machine learning (ML) models, predictive analytics, and advanced visualizations that drive measurable business outcomes. The ideal candidate combines strong analytical skills with deep technical expertise in cloud-based data science tools, particularly within the Databricks and Amazon Web Services (AWS) ecosystem. This role requires a passion for solving complex problems, a collaborative mindset, and the ability to translate data into actionable insights for a midstream energy environment.
Requirements
- Bachelor’s or Master’s degree in Data Science, Computer Science, Engineering, Statistics, or a related field.
- 3–5+ years of experience in applied data science, preferably in the energy, utilities, or industrial sectors.
- Proficiency in Python, SQL, and data science libraries such as pandas, scikit-learn, TensorFlow, or PyTorch.
- Experience working with Databricks and AWS services.
- Strong understanding of statistical modeling, machine learning, and data visualization techniques.
- Ability to communicate complex technical concepts to non-technical stakeholders.
- Experience working with large datasets and real-time or near-real-time data environments.
Nice To Haves
- Experience in the natural gas midstream or broader oil & gas industry.
- Familiarity with MLOps practices and tools for model monitoring and retraining.
- Exposure to geospatial data, sensor data, or SCADA systems.
- Experience with Power BI, Tableau, or similar BI tools.
- Knowledge of data governance, security, and compliance in cloud environments.
Responsibilities
- Design, build, and deploy predictive models and machine learning (ML) algorithms to support asset performance, reliability, and commercial optimization.
- Conduct exploratory data analysis (EDA), feature engineering, and statistical modeling using Python, R, or similar tools.
- Develop time series forecasting, anomaly detection, and classification models for operational and business use cases.
- Apply geospatial and sensor data analytics to support pipeline monitoring, flow optimization, and risk assessment.
- Leverage Databricks and Amazon Web Services (AWS) tools such as SageMaker, Redshift, Simple Storage Service (S3), Lambda, and Glue for model training, deployment, and data access.
- Collaborate with data engineers to ensure models are integrated into production pipelines and dashboards.
- Use version control (e.g., Git), MLFlow and continuous integration/continuous deployment (CI/CD) practices to manage model lifecycle and reproducibility.
- Partner with operations, engineering, and commercial teams to identify high-impact use cases and translate business needs into analytical solutions.
- Present findings and recommendations through compelling data visualizations and storytelling using tools like Power BI.
- Support the development of self-service analytics and promote data literacy across the organization.
- Document model assumptions, limitations, and performance metrics to ensure transparency and trust.
- Monitor model performance and retrain as needed to maintain accuracy and relevance.
- Implement MLOps practices to support scalable, automated model deployment and monitoring.
- Ensure compliance with data governance, privacy, and ethical AI standards.
- Stay current with industry trends and emerging technologies to continuously improve analytical capabilities.
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
