Data Scientist (Level II and Senior) - AI & Scalable Analytics Solutions

Woodward is committed to creating a great workplace for all team members. Our company and its members are committed to acting with integrity, being respectful and accountable to one another, and staying humble and driven, while maintaining the highest professional and ethical standards.

We are steadfastly committed to attracting the best talent across our communities creating a rewarding workplace. Together we are fulfilling our purpose to design and deliver energy control solutions our partners count on to power a clean future.

Woodward supports our members’ wellbeing and regularly benchmarks with other companies in our industry to offer an extensive Total Reward package for this position. Salary will be determined by the applicant's education, experience, knowledge, skills, and abilities, as well as internal equity and alignment with market data. 

• Estimated annual base pay: 

  • Level II:  $77,000(minimum) - $100,000(midpoint) - $123,000(maximum)

  • Level III: $93,000(minimum) - $121,000(midpoint) - $149,000(maximum)

All levels are eligible for the following:

• All members included in annual cash bonus opportunity

• 401(k) match (4.5%)

• Annual Woodward stock contribution (5%)

• Tuition reimbursement and Training/Professional Development opportunities for all members  

• 12 paid holidays, including floating holidays

• Industry leading medical, dental, and vision Insurance upon date of hire

• Vacation / Sick Time / Vacation Buy-up / Short Term Disability / Bereavement leave

• Paid parental leave

• Adoption Assistance  

• Employee Assistance Program, including mental health benefits. 

• Member Life & AD&D / Long Term Disability / Member Optional Life 

• Member referral bonus 

• Spouse / Child Optional Life / Optional AD&D / Healthcare and Dependent Care Flexible Spending 

• Voluntary benefits, including:  

• Home / Auto Insurance discounts 

• Whole Life Insurance / Critical Illness Insurance / Legal Assistance / Military Leave 

About This Role:

This role requires both the analytical depth to develop high-quality models and the technical breadth to deploy and scale them reliably in production. The ideal candidate takes full ownership of the model lifecycle from exploratory analysis and experimentation through deployment, infrastructure integration, and ongoing operational reliability. They partner closely with BI, IT, and cross-functional stakeholders to ensure that AI and machine learning capabilities are embedded durably into the organization’s operations and decision-making processes.

Data Scientist II 

Key Responsibilities:

• Develop Predictive Models: Designs and implement statistical models and machine learning algorithms to analyze complex datasets and generate actionable insights.

• Data Processing and Management: Collects, cleanses, and organizes largescale data from various sources to ensure accuracy and reliability for analysis.

• Collaborate Across Teams: Partners with cross functional teams to understand business requirements and integrate data driven solutions into organizational processes.

• Mentor Junior Team Members: Provides informal guidance and support to new data scientists, fostering skill development and knowledge sharing within the team.

• Communicate Analytical Findings: Presents complex data insights and technical information to stakeholders in a clear and understandable manner, facilitating informed decision making.

• ML Infrastructure Support: Assists in configuring and maintaining the compute and storage infrastructure needed to train and serve models, including cloud-based environments and containerized workflows.

• Feature Engineering & Data Pipelines: Builds and maintains repeatable, automated pipelines for feature extraction, transformation, and loading to support model training and inference.

• Model Packaging & API Integration: Packages trained models for consumption via REST APIs or internal services, enabling downstream integration with business applications and dashboards.

• Version Control & Reproducibility: Applies software engineering best practices including version control (Git), experiment tracking, and environment management to ensure models are reproducible and auditable.

Key Skills:

• Data Analysis: Proficient in analyzing large and complex datasets to identify trends and derive actionable insights.

• Statistical Modeling: Expertise in developing and applying statistical models to support business decision making processes.

• Machine Learning: Skilled in designing, implementing, and evaluating machine learning algorithms for predictive analytics.

• Programming: Advanced proficiency in programming languages such as Python and R for data manipulation and analysis.

• Data Visualization: Ability to create clear and informative visualizations using tools like Tableau, Power BI, or matplotlib.

• Problem Solving: Capable of identifying issues and developing effective solutions using technical expertise and analytical judgment.

• Communication: Effectively conveys complex data findings to nontechnical stakeholders in a clear and understandable manner.

• Business Acumen: Understands key business drivers and aligns data science projects with organizational goals and strategies.

• Collaboration: Works effectively within team environments, providing informal guidance and support to new team members.

• Data Management: Knowledgeable in data warehousing, data cleaning, and database management to ensure data integrity and accessibility.

• Software Engineering Practices: Familiarity with Git, CI/CD basics, and containerization fundamentals to support reproducible and maintainable model development workflows.

• Cloud Platforms: Exposure to cloud compute, storage, and model serving services (Azure, AWS, or GCP) for scalable model training and deployment.

• API Development Fundamentals: Basic understanding of REST API design and frameworks such as FastAPI or equivalent for model integration.

• Experiment Tracking & Reproducibility: Familiarity with tools such as MLflow or DVC to track experiments, manage model versions, and ensure reproducibility.

Sr Data Scientist

Key Responsibilities:

• Develop Advanced Data Models: Designs and implement sophisticated statistical and machine learning models to analyze complex datasets and generate actionable insights.

• Lead Data Projects: Manages end-to-end data science projects, ensuring timely delivery and alignment with business objectives.

• Mentor Team Members: Provides expertise and support to junior data scientists, fostering skill development and knowledge sharing.

• Collaborate with Cross Functional Teams: Partners with business units, IT, and other departments to integrate data solutions into organizational strategies.

• Solve Complex Data Challenges: Identifies and addresses intricate data problems, applying innovative methodologies and leveraging multiple information sources.

• Scalable ML Architecture Design: Designs and owns the architecture of scalable ML systems, including training pipelines, model serving infrastructure, and real-time or batch inference patterns appropriate for enterprise-scale use cases.

• ML Platform Leadership: Drives adoption and governance of the ML platform, establishing standards for tooling, environments, and workflow orchestration (e.g., Azure ML, Databricks, Airflow, or equivalent).

• CI/CD for ML: Implements and maintains continuous integration and deployment pipelines for machine learning models, ensuring automated testing, validation, and promotion of models through development, staging, and production environments.

• Cross-Functional Engineering Collaboration: Partners with IT, data engineering, and software development teams to integrate ML outputs into operational systems, ensuring models are accessible, secure, and maintainable at scale.

• Cost & Performance Optimization: Evaluates and optimizes compute resource utilization for model training and serving, balancing performance requirements against infrastructure cost.

Key Skills:

• Data Analytics: Expertise in analyzing large datasets to extract actionable insights.

• Machine Learning: Proficiency in developing and deploying machine learning models.

• Statistical Modeling: Advanced knowledge of statistical techniques and their application.

• Project Management: Skilled in managing data science projects from initiation to completion.

• Programming: Proficiency in languages such as Python and R for data manipulation and analysis.

• Communication: Ability to explain complex data concepts to nontechnical stakeholders.

• Problem Solving: Aptitude for solving complex, data driven problems with innovative solutions.

• Data Visualization: Expertise in creating clear and impactful visual representations of data.

• Business Acumen: Understanding of industry trends and how data science integrates with business strategies.

• Mentorship: Ability to guide and support less experienced colleagues in data science practices.

• Scalable ML Systems Design: Experience with distributed training, model serving, and understanding of latency/throughput trade-offs at enterprise scale.

• Cloud-Native ML Engineering: Proficiency with managed ML platforms such as Azure ML or Databricks, including managed endpoints and autoscaling.

• Software Engineering Proficiency: Strong command of object-oriented design, testing frameworks, code review practices, and CI/CD pipelines applied to ML workflows.

• Containerization & Orchestration: Working knowledge of Docker, Kubernetes, or managed equivalents for packaging and deploying models reliably in production.

• Infrastructure-as-Code Awareness: Familiarity with tools such as Terraform or Bicep for reproducible and auditable environment provisioning.

Application window is anticipated to close 7 days from original posting date.

This information is provided in compliance with the Colorado Equal Pay for Equal Work Act and is the company’s good faith and reasonable estimate of the compensation range and benefits offered for this position. The compensation offered to the successful applicant may vary based on factors including experience, skills, education, location, and other job-related reasons.

This position requires use of information which is subject to the International Traffic in Arms Regulations (ITAR) and/or the Export Administration Regulations (EAR).   All applicants must be U.S. Persons within the meaning of the ITAR and EAR, or eligible to obtain all required authorizations from the U.S. Department of State and/or the U.S. Department of Commerce. The ITAR defines a U.S. Person as a U.S. citizen or national, lawful permanent resident (i.e., 'Green Card holder'), or a protected person (e.g., asylee, or refugee).

Woodward is an equal opportunity employer and does not discriminate in hiring or employment on the basis of race, color, religion, sex (including sexual orientation and gender identity), national origin, age, disability, protected veteran status, or any other category protected under federal, state, or local laws.