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

Woodward L'Orange•Rockford, IL

17h

About The Position

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.

Requirements

Proficient in analyzing large and complex datasets to identify trends and derive actionable insights.
Expertise in developing and applying statistical models to support business decision making processes.
Skilled in designing, implementing, and evaluating machine learning algorithms for predictive analytics.
Advanced proficiency in programming languages such as Python and R for data manipulation and analysis.
Ability to create clear and informative visualizations using tools like Tableau, Power BI, or matplotlib.
Capable of identifying issues and developing effective solutions using technical expertise and analytical judgment.
Effectively conveys complex data findings to nontechnical stakeholders in a clear and understandable manner.
Understands key business drivers and aligns data science projects with organizational goals and strategies.
Works effectively within team environments, providing informal guidance and support to new team members.
Knowledgeable in data warehousing, data cleaning, and database management to ensure data integrity and accessibility.
Familiarity with Git, CI/CD basics, and containerization fundamentals to support reproducible and maintainable model development workflows.
Exposure to cloud compute, storage, and model serving services (Azure, AWS, or GCP) for scalable model training and deployment.
Basic understanding of REST API design and frameworks such as FastAPI or equivalent for model integration.
Familiarity with tools such as MLflow or DVC to track experiments, manage model versions, and ensure reproducibility.
Proficiency in developing and deploying machine learning models.
Advanced knowledge of statistical techniques and their application.
Skilled in managing data science projects from initiation to completion.
Ability to explain complex data concepts to nontechnical stakeholders.
Aptitude for solving complex, data driven problems with innovative solutions.
Expertise in creating clear and impactful visual representations of data.
Understanding of industry trends and how data science integrates with business strategies.
Ability to guide and support less experienced colleagues in data science practices.
Experience with distributed training, model serving, and understanding of latency/throughput trade-offs at enterprise scale.
Proficiency with managed ML platforms such as Azure ML or Databricks, including managed endpoints and autoscaling.
Strong command of object-oriented design, testing frameworks, code review practices, and CI/CD pipelines applied to ML workflows.
Working knowledge of Docker, Kubernetes, or managed equivalents for packaging and deploying models reliably in production.
Familiarity with tools such as Terraform or Bicep for reproducible and auditable environment provisioning.

Responsibilities

Designs and implement statistical models and machine learning algorithms to analyze complex datasets and generate actionable insights.
Collects, cleanses, and organizes largescale data from various sources to ensure accuracy and reliability for analysis.
Partners with cross functional teams to understand business requirements and integrate data driven solutions into organizational processes.
Provides informal guidance and support to new data scientists, fostering skill development and knowledge sharing within the team.
Presents complex data insights and technical information to stakeholders in a clear and understandable manner, facilitating informed decision making.
Assists in configuring and maintaining the compute and storage infrastructure needed to train and serve models, including cloud-based environments and containerized workflows.
Builds and maintains repeatable, automated pipelines for feature extraction, transformation, and loading to support model training and inference.
Packages trained models for consumption via REST APIs or internal services, enabling downstream integration with business applications and dashboards.
Applies software engineering best practices including version control (Git), experiment tracking, and environment management to ensure models are reproducible and auditable.
Manages end-to-end data science projects, ensuring timely delivery and alignment with business objectives.
Provides expertise and support to junior data scientists, fostering skill development and knowledge sharing.
Partners with business units, IT, and other departments to integrate data solutions into organizational strategies.
Identifies and addresses intricate data problems, applying innovative methodologies and leveraging multiple information sources.
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.
Drives adoption and governance of the ML platform, establishing standards for tooling, environments, and workflow orchestration (e.g., Azure ML, Databricks, Airflow, or equivalent).
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.
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.
Evaluates and optimizes compute resource utilization for model training and serving, balancing performance requirements against infrastructure cost.

Benefits

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