RESPONSIBILITIES
- Use statistical modeling to shape the our data strategy for data acquisition (real and synthetic), validation and ML training
- Develop metrics and frameworks to understand the distribution and diversity of data
- Tackle ambiguous problems using data-driven analysis, and provide actionable insights to inform decision making and demonstrate business impact
- Communicate findings on complex technical topics to stakeholders across engineering leadership and product
- Embed with engineering teams to understand and provide data-driven recommendations on their domain-specific challenges
- Drive the adoption of best practices in data science across the organisation, lead other data science engineers
MINIMUM QUALIFICATIONS
- Industry experience using Python for data science (e.g. numpy, scipy, scikit, pandas, etc.) and SQL or other languages for relational databases.
- Experience with a cloud platform such as (AWS, GCP, Azure etc.)
- Experience with common data science tools; statistical analysis, mathematical modelling etc.
- Experience in developing analytical frameworks to facilitate data-driven decision making in the face of high ambiguity
- A track record of building relationships with engineering and product leadership, and influencing strategic business decisions
- Ability to communicate concepts clearly and precisely to technical and non technical stakeholders
- Experience working in a cross-functional environment
NICE TO HAVES
- Experience in working in a production ML environment
- Experience working with geographically distributed teams
- Previous experience in the AD/ADAS domain or adjacent fields (e.g. robotics)
- Experience with temporal data and/or robotics sensor data.

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