Data Scientist

• Assess, interrogate, and improve existing machine learning models that are currently underperforming.
• Identify opportunities to enhance model accuracy, performance, and business value.
• Train, tune, and optimize machine learning models, including addressing overfitting, underfitting, and other performance issues.
• Develop and refine imputation models to address missing metadata and feature gaps within the data.
• Analyze data quality challenges and determine the impact of missing information on model performance.
• Evaluate whether pursuing additional data collection efforts is worthwhile from both a cost and modeling perspective.
• Determine appropriate sample sizes and data requirements to ensure modeling efforts are neither over- nor under-scoped.
• Partner with Data Engineers while maintaining sufficient data pipeline knowledge to review and validate pipeline logic and implementation.
• Contribute to the overall production machine learning ecosystem, including model maintenance, versioning, and operational best practices.
• Help the team meet near-term delivery deadlines where current modeling resources have not met expectations.
• Communicate findings, recommendations, and tradeoffs effectively to both technical and non-technical stakeholders.

Technical Environment

• Production environment is hosted in AWS.
• Dedicated Data Engineering resources are responsible for building production pipelines.
• Dedicated MLOps resources are available, although familiarity with MLOps concepts would be beneficial.
• Models are already operating in production and require ongoing enhancement and optimization.

Ideal Candidate Profile

• Strong machine learning practitioner with a focus on modeling rather than platform engineering.
• Deep understanding of classical machine learning techniques, including regression modeling.
• Experienced in diagnosing and improving model performance.
• Strong analytical mindset with the ability to assess data quality and feature availability challenges.
• Pragmatic problem solver capable of balancing model improvement against business effort and cost.
• Effective communicator who can clearly explain technical decisions and recommendations.
• Familiarity with production-grade machine learning systems and operational considerations.

Must-Have Skills:

• Machine Learning model development and optimization
• Classical machine learning algorithms
• Regression modeling
• Model training, evaluation, and performance tuning
• Experience addressing overfitting and underfitting
• Feature engineering and missing data/imputation techniques
• Strong statistical analysis and sampling methodology knowledge
• Data quality assessment and root-cause analysis
• Python-based machine learning development
• Experience with production machine learning systems
• Model versioning and lifecycle management
• Ability to review and understand data pipeline code
• Strong communication and stakeholder engagement skills
• Pragmatic, business-focused problem-solving approach

Nice-to-Have Skills:

• AWS experience
• MLOps concepts and tooling
• Model deployment and monitoring experience
• CI/CD for machine learning
• Energy, oil & gas, commodities, or refining industry experience
• Experience working with large-scale industrial or manufacturing datasets

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