Senior Data Scientist

POSITION SUMMARY

The Senior Data Scientist will play a crucial role in leading efforts to design, develop, and enhance analytical tools that address complex business challenges. This position will drive high-value modeling initiatives and experimentation, ensuring analytical rigor and innovation at every stage. A critical focus of this position is the ability to operationalize solutions, embedding advanced analytics into core processes and delivering scalable, production-ready capabilities that create measurable business impact. The individual in this role will:

• Lead and influence technical direction within projects, shaping methodologies and best practices.
• Mentor and develop junior team members, fostering growth while advancing your own expertise.
• Innovate and expand our suite of analytical solutions to strengthen organizational capabilities.
• Advance research in priority areas, ensuring robust analysis precedes modeling and experimentation.

This position offers the opportunity to make a significant impact executing complex projects, elevating team performance, deliver actionable insights that influence business decisions, and embed advanced analytics into core processes.


This position will be based on-site in Appleton, WI with in office expectations Monday - Thursday and remote work Fridays as business needs allow.

JOB RESPONSIBILITIES

Development:

• Apply data science fundamentals: data collection, cleansing, modeling, and visualization.
• Develop solutions in Python and SQL to solve complex problems.
• Perform advanced data analysis and hypothesis testing to validate assumptions and guide modeling decisions.
• Apply modern software engineering principles for data science (CI/CD, Git, object-oriented programming, containerization).
• Perform advanced statistical modeling techniques, including classification and regression (ML/AI) 
• Optimization model methodologies 
• Forecasting model development and management 
• Architect scalable, reliable model services with monitoring and retraining strategies.
• Establish robust evaluation frameworks (bias and robustness checks, ablations, model cards).

Innovation

• Lead innovation and data research efforts for new concepts and technologies.
• Coordinate and facilitate technical brainstorming and ideation sessions.
• Build proof-of-concepts and prototypes to validate emerging approaches.
• Advance research initiatives in areas such as LLM/RAG evaluation & safety, AI agent frameworks, generative AI applications (text, vision, multimodal), advanced optimization, and causal ML.
• Develop reusable accelerators and standards to shorten time-to-value for future projects.
• Mentor junior team members in innovative solution development.
• Stay ahead of industry trends and proactively identify opportunities to integrate cutting-edge techniques into business processes.
• Champion responsible AI practices in all innovation efforts.

Execution

• Lead problem framing and EDA to ensure the right method for the problem.
• Own full lifecycle: model → experiment → pilot → monitoring → decision/adoption.
• Drive prioritization and trade-off decisions to balance accuracy, scalability, and time-to-value.
• Deliver executive‑ready decision briefs and technical documentation.
• Collaborate with platform and product teams to ensure seamless integration and operational reliability.

Collaboration:

• Embrace mentorship of junior team members and code reviews; guide method selection and documentation.
• Coordinate with Platform for business‑critical productionization and SLO alignment.
• Build strong relationships and establish credibility with stakeholders at all levels, including senior leadership, through effective interpersonal and communication skills.

QUALIFICATIONS

 

• Bachelor’s or Master’s degree in Data Science, Data Analytics, Economics, Statistics, Computer Science or a related field involving problem solving and critical thinking, or equivalent work experience. 

• 5+ years working in data science-specific roles, with demonstrated experience working in roles that require proficiency with data modeling, statistical modeling, model deployment, data management with evidence of strategic impact.

• Proven project experience working in ML and experimentation; strong software design instincts and observability practices.

• Expertise in Python and SQL, with the ability to develop production-quality code using modern software engineering practices (e.g., object-oriented design, version control with Git, CI/CD pipelines

• Proven ability to translate complex