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The Product Operations Data Team is looking for an analytically sharp, intellectually curious individual to own the data optimization vision for our Capex Equipment Engineering organization. This is not a model-building role - it is an architectural and strategic one. You will serve as the critical bridge between deep Capex domain knowledge and the technical capabilities of a dedicated ML engineering team, translating what the business needs to predict into what the models need to learn. This is a high-growth opportunity for a driven, curious individual who is ready to own something significant and expand their impact as the vision scales.
Description
In this role you will define, shape, and drive the data optimization frameworks that transform how the Capex team operates - shifting from manual estimation to model-driven prediction that influences product design decisions before commitments are made.
Minimum Qualifications
3+ years of experience in an analytical, data, or technically oriented role
BS or MS degree in Computer Science, Engineering, Data Science, or a related quantitative field, or equivalent hands-on experience
Strong quantitative analytical skills - comfortable working with complex, multi-source datasets to extract meaningful signals
Foundational understanding of how predictive models work - what they require as inputs, how they are trained, and how their outputs should be interpreted and validated
Demonstrated ability to translate ambiguous business problems into structured, precise requirements that a technical team can act on
Preferred Qualifications
5+ years of experience in an analytically driven role with increasing scope and ownership
Some exposure to manufacturing, supply chain, or capital equipment environments - enough to engage credibly with domain concepts and recognize when a model output makes operational sense
Experience working at the interface between business and engineering teams, serving as a translator or connector across functions
Familiarity with data pipeline concepts, feature engineering, and model validation practices - even without hands-on model building experience
Experience defining requirements for ML or data products and partnering with technical teams through the development lifecycle
Clear and confident communicator, able to represent team needs to a technical audience and explain complex analytical concepts to non-technical stakeholders
Demonstrated intellectual curiosity and a track record of growing technical depth independently in a fast-moving environment
Comfortable operating in ambiguous, early-stage problem spaces where the framework itself is still being defined
Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.
- Dice Id: 90733111
- Position Id: 9991bb576a40c4bea6e025b62d2c0d06
- Posted 1 day ago
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