Senior Data Scientist
Role details
Job location
Tech stack
Job description
The Senior Data Scientist is a strategic hire within the Operations organization, responsible for solving RJW's most complex operational questions through modeling, optimization, and applied data science. This role goes beyond reporting and dashboards - it exists to answer questions that cannot be resolved by querying alone: how labor should be structured, how product should be slotted, how routes should be sequenced, and how volume should be allocated across the network. Reporting to the EVP of Warehouse Transformation, this individual will set the standard for how RJW understands and uses its data, leads the analytical roadmap for the Operations function, and serve as the technical demand signal that shapes the data foundation RJW builds. As the senior-most data science practitioner embedded in Operations, this role will define what good looks like for a maturing analytics practice and ensure that insights translate directly into measurable operational outcomes., * Frame, scope, and lead analytical investigations into RJW's highest-value operational questions, translating ambiguous business problems into structured, modelable work.
- Design, develop, and deploy statistical models, optimization algorithms, and decision-support tools that drive measurable improvements in labor utilization, throughput, network cost, and service performance.
- Own the analytical roadmap for the Operations function, prioritizing problems based on business impact, feasibility, and data readiness in partnership with the EVP of Warehouse Transformation and operational leaders.
- Lead modeling efforts across a portfolio of strategic problem areas - examples include shift and labor structure optimization, network-level volume allocation and "right fit" modeling, warehouse slotting strategy, and improvement of internal route and task optimization algorithms.
- Define the data, infrastructure, and tooling requirements needed to support advanced analytics at RJW, partnering closely with the Data Engineering team to make critical data available, accessible, and where appropriate, calculated in real time.
- Serve as a technical thought partner to the Data Engineering team, providing direction on data models, latency requirements, and analytical surface area without owning platform governance.
- Collaborate with the Operations Data Analytics team to ensure tactical analyst work and strategic data science work reinforce one another rather than duplicate effort.
- Translate model outputs into clear, actionable recommendations for operational leaders, executives, and cross-functional stakeholders - including the CWO and broader executive team where appropriate.
- Establish standards for analytical rigor, model validation, documentation, and reproducibility within the Operations analytics function.
- Evaluate and recommend tools, platforms, and methodologies that advance RJW's analytical capability over time.
- Mentor and influence analysts, engineers, and operational stakeholders on the appropriate use of data, models, and statistical reasoning.
- Bring in specialized expertise (consultants, vendor SMEs, internal partners) when problems require depth outside the scope of any single practitioner.
- Stay current with developments in applied data science, operations research, and warehouse and supply chain analytics, recommending adoption where it advances RJW's operational performance.
Requirements
Do you have experience in Tooling?, Do you have a Bachelor's degree?, * Strong applied data science background with demonstrated experience framing and solving operational, supply chain, or logistics problems through modeling and optimization.
- Proficiency in Python (pandas, scikit-learn, modern ML and optimization libraries) and SQL; comfort with notebook and production environments.
- Working knowledge of optimization techniques (linear and integer programming, heuristics, simulation) and statistical modeling methods (regression, time series, classification, clustering).
- Ability to scope problems independently - deciding what is worth modeling, what is a data problem, and what is a process problem - and to recognize the limits of one's own expertise.
- Demonstrated ability to communicate complex analytical concepts to operational leaders and executives in plain language, with a focus on decisions and outcomes rather than methods.
- Self-starter comfortable operating with significant autonomy in a maturing analytics environment; energized by ambiguity and the opportunity to set direction.
- Experience defining data requirements and partnering with engineering teams to bring those requirements to life, without taking on platform ownership.
- Strong product and outcome orientation - the ability to drive a model or tool from concept to operational adoption, not just to a notebook deliverable.
- Ability to integrate insights from vendors, consultants, and internal subject matter experts into a coherent analytical approach.
- Demonstrated judgment in selecting techniques appropriate to the problem and data available, balancing rigor with speed-to-insight.
- Collaborative working style; effective across Operations, IT, Continuous Improvement, and executive stakeholders.
- High integrity in handling data, models, and recommendations that influence operational and financial decisions., * Bachelor's degree in a quantitative field (Data Science, Statistics, Mathematics, Operations Research, Computer Science, Economics, Industrial Engineering, or related) required; advanced degree preferred.
- 7+ years of professional experience in applied data science, machine learning, or operations research, with demonstrated impact on operational or business outcomes.
- Experience leading or substantially contributing to data science work in supply chain, logistics, warehousing, transportation, or comparable operational domains strongly preferred.
- Experience working with optimization problems (routing, allocation, slotting, scheduling, or similar) preferred.
- Track record of working directly with executive and operational leaders to influence decisions through data.
- Experience helping mature an analytics or data science function - including shaping data strategy, partnering with data engineering, and establishing standards - preferred.
Work Environment:
- While performing the duties of this Job, the employee is occasionally exposed to moving mechanical parts, and fumes or airborne particles. The noise level in the work environment will range from quiet to moderately loud.
Benefits & conditions
Pulled from the full job description
- Health insurance
- 401(k) matching
- Paid time off
- Employee discount
- Vision insurance
- Health savings account
- Dental insurance, * MyShare Program (explained above)
- 401(k) matching
- Medical/Dental/Vision insurance
- Employee discount
- Flexible spending account
- Health savings account
- Paid time off
- Sick Days
- Long-term Disability Insurance
- Short-term Disability Insurance
- Accidental Insurance
- Critical Illness Insurance
- Disability Insurance
Req Benefits: We offer a competitive benefits package that includes health, dental, and vision insurance, life and disability, paid holidays, time off, retirement savings plan participation, and additional employee programs. Pay Transparency In order to support the Fair Compensation Strategy by the US Govt., HR Dept., clients are required to adhere to "Pay Transparency Law"; in the impacted states; that have mandated the employers to list the salary ranges in Job advertisements or postings for job opportunities and Job promotions.