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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in data science and AI project leadership, with a focus on causal inference, uplift modeling, and optimization techniques. Proficient in developing scalable machine learning models and production-grade code while effectively communicating complex concepts to stakeholders.
Highest-signal resume keywords
Data Science Project LeadershipCausal Inference ExpertiseUplift ModelingProduction-Quality Python CodingMLOps Tools Experience
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Causal InferenceUplift ModelingOptimization TechniquesA/B TestingRandomized Controlled TrialsProduction-Quality PythonSQLBigQueryMachine Learning PipelinesData Analysis
Soft Skills
Excellent CommunicationStakeholder InfluenceMentoring
Tools & Technologies
DockerAirflowDagsterGCPVertex AIAWSSageMaker
Industry Keywords
E-CommerceMarketplaceOn-Demand DeliveryConsumer-Facing Tech
Tech Stack
Tools & technologiesAirflowAWSBigQueryCloudDockerGoogle Cloud PlatformPythonSQL
About the role
Key responsibilities & impact- Drive the intelligence behind marketing campaigns
- Build models that optimize offer allocation across millions of users and global markets
- Lead end-to-end data science and AI projects from ideation and model design to production deployment
- Apply causal inference, uplift modelling, optimisation techniques, and advanced LLM tools to solve complex marketing decisions
- Evaluate the real-world impact of strategies using causal measurement
- Identify opportunities to improve promotion efficiency and customer experience
- Ensure technical solutions are robust, scalable, and impactful
- Take ownership of complex data science projects to reduce wasted spend and drive order growth
- Develop models predicting incremental impact per user, offer, and market
- Design and deploy scalable, production-grade code and ML pipelines integrated with cloud infrastructure
- Apply causal inference methods and randomized controlled trials to measure true incremental impact across markets
- Partner with leadership and translate complex model outcomes into clear business decisions
- Champion coding standards, conduct code reviews, and mentor colleagues
Requirements
What you’ll need- Proven experience as a Data Scientist, with a strong portfolio of delivering impactful projects in a commercial environment
- Deep theoretical and practical knowledge of uplift modelling, causal inference (meta-learners, treatment effect estimation), and experimentation (A/B testing, RCTs)
- Experience with optimisation techniques, including budget-constrained allocation and knapsack problems
- Experience with advanced AI tools and decision-focused learning
- Comfortable writing production-quality Python code
- Experience with MLOps tools, containerisation (Docker), and orchestration (Airflow/Dagster)
- Experience with cloud platforms (GCP/Vertex AI preferred, or AWS/SageMaker)
- Experience handling large datasets via SQL/BigQuery
- Experience in e-commerce, marketplace, on-demand delivery, or consumer-facing tech industries is a strong asset
- Excellent communication skills, with the ability to explain complex AI concepts to non-technical stakeholders and influence decision-making