Senior Data Scientist

Fictiv•Oakland, CA

1h•Onsite

About The Position

Fictiv Exists to Enable Hardware Innovators to Build Better Products, Faster Fictiv exists to help product innovators create. Fictiv is a global manufacturing and supply chain company that enables organizations to scale globally across Fictiv’s four global manufacturing centers in India, Mexico, China, and the U.S.. Companies use Fictiv to access high-quality production, optimize supply chain logistics, and mitigate supply chain risk—ensuring they can move from prototype to full-scale manufacturing with speed and confidence. To date, Fictiv has delivered more than 35 million commercial and prototype parts for industries such as aerospace, robotics, automotive, climate tech, and more, helping them innovate faster, free up precious resources, and drive profitable growth. This role is based in our Oakland, CA office, with 2-3 days per week required in the office Impact In This Role As a Senior Data Scientist, you will be a key driver in identifying and solving complex business challenges using data. You will partner with stakeholders across Operations, Supply Chain, Finance, Product, and Engineering to define problems, explore opportunities for optimization, and design data science solutions that improve efficiency, scalability, and profitability. You’ll take projects from ideation through deployment — including data exploration, feature engineering, model development, validation, and integration into production workflows. This is a hands-on, impact-oriented role where you will work directly with business and technical partners to develop predictive models, optimization frameworks, and AI-driven tools that enhance Fictiv’s core operations — from scheduling and vendor capacity management to pricing, costing, and workflow automation. You’ll also ensure that data science initiatives are built on sound statistical foundations, follow best practices, and deliver meaningful, interpretable outcomes. In addition, you’ll collaborate with the Data Analytics Manager and the broader Data Engineering & Analytics team to scale Fictiv’s data capabilities. You’ll mentor peers, contribute to the development of shared methodologies, and help establish standards for modeling, experimentation, and data quality across the organization. Your work will directly influence Fictiv’s ability to operate more efficiently, innovate faster, and deliver value to both customers and manufacturing partners. You will report to the Data Analytics Manager.

Requirements

M.S. in Data Science, Computer Science, Statistics, Applied Mathematics, Operations Research, or a related quantitative field (Ph.D. preferred)
10+ years of experience in data science and machine learning with a proven track record of delivering measurable business impact
Hands-on experience with machine learning algorithms (e.g., regression, classification, clustering, forecasting, reinforcement learning) and model evaluation frameworks
Hands-on experience preparing and curating datasets for machine learning or AI research, including cleaning, transformation, labeling, and creation of synthetic or anonymized data for model training or simulation purposes
Demonstrated experience in experimental design, hypothesis testing, and A/B test evaluation, with a clear understanding of sampling, control groups, and significance testing in applied business contexts
Demonstrated expertise in Python (pandas, numpy, scikit-learn, etc.) and SQL, with the ability to design efficient data pipelines and feature engineering workflows
Strong background in data exploration, feature engineering, and model validation, with the ability to design experiments and evaluate model performance rigorously
Demonstrated skill in developing and operationalizing ML-driven solutions that enhance business workflows while ensuring models are interpretable and scalable
Experience working with modern data platforms such as Snowflake, Fivetran, or dbt, and integrating models into BI or operational systems (e.g., Sigma, Tableau, Power BI)
Strong understanding of data warehousing concepts, dimensional modeling, and data governance principles
Demonstrated ability to perform root cause analysis and use data-driven reasoning to identify opportunities for process improvement or risk mitigation
Demonstrated ability to translate ambiguous business problems into structured data science solutions, from hypothesis through implementation
Working knowledge of data and analytics architecture, data warehousing concepts, and data governance best practices
Proven ability to lead and coordinate cross-functional initiatives, communicating effectively with engineering, product, and business stakeholders
Ability to manage multiple projects simultaneously, operate independently, and thrive in a fast-paced, collaborative environment
Excellent leadership and collaboration skills, with experience mentoring junior data scientists or analysts and leading large, cross-functional initiatives
Passionate about learning new technologies, staying current with emerging AI methods, and applying them pragmatically to create business value
Excellent communication skills — able to present technical findings clearly to both executives and technical audiences
Ability to work effectively across time zones and communicate fluently in professional English, both written and verbal

Nice To Haves

Ph.D. preferred
Experience developing predictive and prescriptive models for pricing, demand forecasting, workflow automation, or vendor performance optimization is highly desirable
Experience applying advanced analytics, predictive modeling, or optimization techniques within manufacturing, supply chain, logistics, or operations-focused environments is a plus
Familiarity with operations research techniques (linear programming, simulation, constraint optimization) and their application to scheduling, resource allocation, and capacity management is a plus

Responsibilities

Lead the scoping, planning, and delivery of high-impact data science projects that address complex business challenges across pricing, supply chain, scheduling, and manufacturing operations
Serve as the subject matter expert on data-driven modeling and machine learning techniques
Design, develop, and deploy predictive and prescriptive models that enable smarter decision-making, improve forecasting accuracy, and drive automation across business workflows
Partner with cross-functional leaders in Operations, Finance, Product, and Engineering to identify opportunities where data science can create a measurable impact and translate those opportunities into scalable solutions
Contribute to the standardization of data science best practices, including model governance, experimentation design, and statistical validation frameworks
Guide the organization in applying proper statistical standards to A/B and multivariate tests, ensuring experiments are well-designed, results are accurately interpreted, and findings are reproducible and actionable
Partner with the AI Research Group to prototype and implement AI-driven solutions to improve workflow efficiency, including intelligent pricing systems, predictive maintenance, and process automation
Prepare, clean, and transform complex datasets to support advanced modeling and AI initiatives, including generating synthetic, anonymized, or simulated data as needed to train or validate machine learning and reinforcement learning systems
Collaborate with Data Engineering to ensure high-quality, reliable data pipelines and feature sets that support advanced analytics and model development
Mentor and guide analysts and junior data scientists, helping elevate modeling rigor, data storytelling, and analytical thinking across the organization
Work closely with product managers and engineering teams to integrate data science capabilities into Fictiv’s digital platform and operational tools
Communicate results clearly and effectively to both technical and non-technical stakeholders, providing actionable insights and recommendations that influence strategic decisions
Document modeling approaches, assumptions, and methodologies to ensure reproducibility and transparency of results
Thrive in a collaborative environment — open to feedback, discussion, and iteration in pursuit of the best solutions