Data Scientist - Senior - Innovative Engines and Energy Company in , United States

Turn complex data into powerful, real-world business outcomes. This role offers the opportunity to work on sophisticated analytical and machine learning challenges within a modern, data-driven environment. You will design and deploy advanced models that uncover insights, drive decision-making, and solve high-impact business problems across large-scale systems.

This position is ideal for someone who enjoys combining statistical rigor, technical engineering, and practical application-building solutions that move beyond theory and deliver measurable results.

Role Overview The Data Scientist / Machine Learning Engineer is responsible for developing advanced analytical models and algorithms that transform large, complex datasets into actionable insights. You will work across the full lifecycle of data science projects-from data exploration and feature engineering to model development, validation, and deployment.

This role requires strong collaboration with technical teams, domain experts, and business stakeholders to ensure that analytical solutions are both technically sound and aligned with real-world needs.

Key Responsibilities

Advanced Analytics & Model Development : Design and implement statistical and machine learning models to solve complex business problems, including classification, regression, anomaly detection, and forecasting.

Data Exploration & Preparation : Analyze large datasets to identify patterns, relationships, and anomalies. Perform data cleaning, transformation, and feature engineering to prepare data for modeling.

Algorithm Design & Optimization : Develop and refine algorithms using advanced statistical techniques and machine learning methodologies to improve model accuracy and performance.

End-to-End Solution Delivery : Build and deploy analytical solutions using modern tools and frameworks, ensuring scalability, reliability, and maintainability in production environments.

Cross-Functional Collaboration : Partner with domain experts, engineers, and architects to align data models with business objectives and technical infrastructure.

Data Pipeline & Architecture Alignment : Work with technical teams to ensure appropriate data flow, storage, and processing architectures support analytical workloads.

Model Validation & Performance Monitoring : Validate models using statistical testing and performance metrics. Monitor models over time to ensure continued accuracy and relevance.

Communication & Reporting : Translate complex analytical findings into clear, actionable insights for both technical and non-technical stakeholders.

Knowledge Sharing & Mentorship : Support team development by sharing expertise, guiding less experienced team members, and contributing to a collaborative learning environment.

Education : Bachelor’s or Master’s degree in Data Science, Computer Science, Information Technology, Statistics, or a related field. Equivalent experience may also be considered.

Programming Skills : Strong proficiency in Python and SQL for data analysis, model development, and data manipulation.

Machine Learning Experience : Hands-on experience working with Databricks in production environments (required); experience developing and deploying predictive models and analytical solutions in real-world applications.

Statistical Knowledge : Strong understanding of statistical methods including hypothesis testing, regression analysis, probability distributions, and time series modeling.

Big Data & Cloud Environments : Experience working with large-scale datasets and distributed computing environments, including cloud platforms such as AWS or Azure.

Problem-Solving Skills : Ability to analyze complex systems, identify root causes, and develop data-driven solutions.

Communication Skills : Strong ability to communicate technical concepts and analytical findings clearly to diverse audiences.

Qualifications

Experience working with the Ray framework for distributed computing.

Familiarity with big data tools and open-source ecosystems.

Experience in Agile development environments.

Knowledge of CI/CD pipelines and model deployment best practices.

Experience validating and testing machine learning systems in production.

Exposure to reliability engineering or system performance domains.

What Makes a Strong Candidate

Ability to bridge the gap between data science theory and practical implementation.

Comfort working in fast-paced environments with evolving project requirements.

Strong analytical mindset with attention to detail and data accuracy.

Collaborative approach to problem-solving and cross-functional teamwork.

Commitment to continuous learning and staying current with emerging data science technologies.

Work Environment This is a fully remote position operating on a standard schedule of

Monday through Friday, 8:00 AM - 5:00 PM EST .

The role is structured as a contract engagement of approximately

six months , with the possibility of extension based on performance and project needs.

Interview Process Selected candidates will participate in a one-hour virtual panel interview as part of the evaluation process.

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