About The Company
Craft.co is a supplier intelligence company dedicated to helping organizations accelerate data-informed business decisions. Our unique, proprietary data platform tracks thousands of real-time signals across millions of companies globally, delivering best-in-class monitoring and insight into global supply chains, among other company cohorts. Our clients, including Fortune 100 companies, government and military agencies, SMEs, and asset management groups, use our technology for supply chain intelligence, market intelligence, and related use cases. Through our modular, secure, customizable portal, clients can monitor any company they are working with and drive critical actions in real-time.
We are a well-funded technology company with leading investors from Silicon Valley and elsewhere. The Craft team is globally distributed with headquarters in San Francisco and an office in London, and we fully support and encourage remote workers across North America, Canada, and Europe.
About The Role
We are looking for a motivated thought leader who loves diving into complex data sets to translate them into meaningful insights, products, and solutions that drive clear outcomes and create value. The ideal candidate is passionate about building a team focused on data frameworks, tools, and models to enable continued data platform scalability and growth of our supply chain insights layer. Through exploration and a culture of educating each other on data literacy, we are looking to create a system of data-informed decision making and language throughout Craft.
You will be responsible for refining Craft’s AI/ML strategy and execution, with the opportunity to make a substantial impact on product, processes, and the company overall. The mission of this role is to expand the use of AI/ML as we continue our journey of being an AI-driven, digital-first company; role focus will include data quality optimization and metrics standardization to substantially elevate and scale our insights layer, including alerts and risk management.
The ideal candidate will have a demonstrated track record of building high-performing teams and delivering scalable data infrastructure and AI/ML driven solutions, with a combined data science and data engineering background. The Head of Data must have a passion for developing a team culture that inspires excellence in driving business results through collaboration. We encourage our teams to take end-to-end responsibility from conceptualization of ideas to implementation and then to measure value created and business impact to drive the highest levels of personal accountability.
In This Role You Will
- Own and drive Craft’s AI/ML strategy; develop a 3-5 year plan on data strategy including aggregated insights and innovative approaches to deliver supply chain intel to our main customer base.
- Evaluate our data sources, coverage, and retention policies to enable us to be more proactive and cost-efficient in our data partnerships.
- Provide digital and technology transformation thought leadership in developing new services, operating models, and use of technology to enable strategic capabilities.
- Grow, mentor, and lead the AI, data science, and engineering teams, ensuring all team members are clear about expected standards of performance, motivated, and developed.
- Collaboratively define specific, measurable, product metrics that relate to automation and ML solutions.
- Further develop our data platform and integrate the latest data models and pipelines, establishing a model management approach that is best in class for responsible data use.
- Provide expertise in statistics and probability, and the evaluation of data use, algorithms, and models. Develop an approach to data science standards to regulate data sources and the impact of AI on our customers.
- Manage the process of designing and running transformation initiatives throughout its entire life-cycle, able to operationalize transformation stages of new product or service development, and overcome operational constraints.
- Follow deployment best practices including integrating ML models with CI/CD. Create and maintain automated evaluation and monitoring.
- Be conversant with relevant legislation and political, social, legal, and technological developments that impact information rights (privacy by design).
- Partner with business leaders and product managers in problem framing, conceptualization of ideas, developing consensus, and executing on a prioritized AI/ML roadmap.
- Oversee all phases of AI/ML development, from design, data gathering, training, validation, and implementation; expand the use of dynamically updated AI/ML models.
- Manage a suite of data science tools/platforms, pipelines, and reusable code that maximizes productivity and knowledge sharing.
- Partner with data engineers to build a continuous data capture service leveraging AWS and expand feature store to include new families of data and real-time streaming data.
- Collaborate with data and machine learning engineers to design and develop scalable machine learning systems (e.g., building a model execution service leveraging MLflow and SageMaker) to improve speed to market and operate with scale in production.
- Partner with a cross-functional team of data engineers, machine learning engineers, and product managers to launch AI/ML solutions into production.
- Create automated AI and model performance monitoring that aligns with model risk management policy.
What We’re Looking For
- 10+ years of experience in experimentation, product analytics, analytics platforms, risk modeling, and/or user research.
- 5+ years of experience building and managing high-performing data science teams, including recruitment, career development, mentoring, and talent management.
- 5+ years of experience leveraging cloud-based machine learning, data infrastructure, and automation to deliver AI/ML driven solutions to solve business problems.
- 5+ years of experience leveraging modern machine learning toolsets and programming languages such as Python, R or Scala.
- 5+ years of experience leveraging various machine learning algorithms (e.g., Gradient Boosting, Random Forest, Bayesian Optimization, neural networks, etc.).
- Exceptional background in at least one of the following areas: advanced experiment design, causal inference, or mixed-methods user research.
- Exceptional ability to translate between business and technical audiences, especially in summarizing highly technical analyses.