As an Engineering Manager, Data Science you are an accomplished data scientist and software engineer, having led successful projects as an individual contributor before directly managing teams of four or more engineers and data scientists. You are fully comfortable with agile management practices, and are a

proven project manager who has delivered many large projects successfully. As a technical leader you are constantly tracking the team’s efficiency and quality of deliverables at SDLC and regularly adjusting processes and timelines to ensure high quality is delivered. You take responsibility for producing models and software that are highly available, monitorable, and maintainable over time.

While in this role, you will spend more time managing, there is an expectation that you will still be heavily involved in technical decision-making, model building, and writing code, without blocking or slowing down the progress of your team. You hold responsibility for identifying bottlenecks in the process and roadblocks to success for your team and clearing these roadblocks.

In this role your success will be determined by the success of your team. In particular, as a leader you are capable of identifying the most high-value projects and keeping your team focused on these projects. As part of keeping the team focused you will partner closely with business stakeholders to manage project scope and ensure the technical deliverables are met. In addition to focusing the team, you are capable of identifying headcount needs for the team and planning and recruiting to fill these needs.

As an Engineering Manager you operate as and are comfortable managing team members with different skill sets from your own. You communicate expectations clearly to all team members, solicit and deliver individual feedback frequently (not just in the scope of review periods). In addition to strong management skills, as the engineering lead you act as a leader for the technical roadmap for the pillar. You are able to clearly communicate the timeline, scope and risks to your pillar partners, and lead the delivery of major initiatives on clear timelines. Additionally, you identify areas of strategic technical debt, do the cost/benefit analysis for resolving this debt and communicate suggested timelines for prioritizing this to the management team.

Above all, you want to build something that fundamentally improves the lives of families.

• Build a data science team at HopSkipDrive by determining our hiring needs, writing job descriptions, screening candidates, writing and reviewing technical assessments, and conducting interviews
•  Work with business stakeholders to identify the low-hanging fruit at HopSkipDrive for building statistical machine learning and/or LLM models to solve high-impact problems
• In conjunction with the DevOps team, architect and create the data science pipeline to rapidly iterate on models and deploy them for use in HopSkipDrive’s production systems
• Identify and clear bottlenecks and roadblocks to the team’s success
• Conduct weekly 1:1s with direct reports
• Performance management - providing positive and constructive feedback
• Foster Continuous Improvement mindset amongst team
• Responsible for the successful planning and delivery of projects
• Review Pull Requests and promote high code quality and testing standards on the team
• Identify areas of strategic technical debt, do the cost/benefit analysis for resolving this debt and
• communicate suggested timelines for prioritizing this to the management team
• Break up large features and long-term initiatives into smaller deliverables
• Collaborate with VP of Engineering on technical roadmap
• Continue to contribute to feature development and bug fixes at the level of a Sr. Software Engineer
• Exhibit high standards when Interviewing and evaluating prospective engineering candidate

• 5+ years of professional experience as a data scientist or software engineer
• 3+ years of professional Python
• 3+ years of professional SQL
• 3+ years shipping production data models in a business environment
• 2+ years managing software engineers and/or data scientists
• Strong expertise designing data collection and data storage processes for model building
• Skilled at testing and retraining models after deployment
• Excellent communication skills with technical and non-technical teammates and show a high level
• of emotional and professional maturity.
• A high level of craftsmanship about your work, the end experience of the user, and impact on
• overall product
•  Expertise with Agile Software Development practices and able to adopt the appropriate process
• to fit the needs of a team
• Successful track record of delivering high-impact projects in a fast-paced environment