Staff Scientist, Tech

About the Role

You'll be the Science Tech Lead (Individual Contributor) for a team of 5-7 members based in India, working closely with Engineering and Product leads to create and execute the direction of the product development. This includes crafting machine learning, statistics, or analytical solutions to achieve business goals, guiding the team's direction through data-driven insights and algorithmic thinking, mentoring scientists and establishing cross functional relationships with partner teams at different locations.

Basic Qualifications

• Undergraduate and/or graduate degree in Statistics, Machine Learning, Operations Research, or other quantitative fields.
• 9+ years of industry experience as an (Applied / Data) Scientist or equivalent.
• Experience with exploratory data analysis, statistical analysis and testing, causal analysis and ML model development.
• Experience in experimental design and analysis.
• Proficiency using Python, Spark at scale with large data sets.
• Experience with tools like SQL, R in a production environment.

Preferred Qualifications

• Master's/Ph.D. Degree in Statistics, Machine Learning, Operations Research, or other quantitative fields.
• Experience in tech or marketplace industries.
• Experienced in partnering with cross-functional stakeholders to execute decisions.
• Ability to mentor, coach and develop junior team members.
• Superb communication and organisation skills.

What the candidate will do

• Collaborate with Engineering and Product leadership to define the technical vision and guide team direction by identifying opportunities and creating data-driven solutions.
• Design innovative solutions and develop prototypes for business challenges using machine learning and statistical algorithms. Establish and guide data analysis and experimentation frameworks to generate business insights and support decision-making.
• Work closely with Engineering and Product teams to deploy models, improve data observability and metrics, and execute experiments and detailed analyses.
• Design and evaluate A/B tests, switchback experiments and quasi-experiments, applying best practices in experimental methodology to ensure high-quality, unbiased insights.
• Partner with Scientists and Engineers to build and enhance the availability, integrity, accuracy, and reliability of data logging and data pipelines.
• Provide mentorship and technical leadership to other Scientists, promoting a culture of statistical excellence and continuous learning.
• Communicate effectively with leadership and cross-functional teams, presenting the Science team's work on models, solutions, and data analytics outcomes.