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: