Data Scientist

We are seeking a Data Scientist to join our Data & AI practice. This is a role for a pragmatic problem-solver who can translate business challenges into data-driven solutions for world-renowned brands. You will apply statistical thinking and machine learning techniques to deliver predictive capabilities that drive measurable marketing and customer experience outcomes—and you'll be comfortable taking your models beyond the notebook into production environments.

This is a hands‑on role where you will deliver impactful analytics and machine learning solutions, primarily leveraging out‑of‑the‑box platform capabilities while applying solid statistical foundations to ensure rigorous, trustworthy results.

• Developing and deploying predictive models (propensity, churn, lookalike) and recommendation systems for targeted campaigns and personalization.
• Conducting deep customer analytics (segmentation, LTV, behavioral analysis) to generate actionable insights.
• Implementing ML solutions using both platform‑native capabilities and custom development, ensuring models are production‑ready and AI‑consumable.
• Maintaining statistical rigor in all methodologies, from experiment design to model validation, supported by robust data exploration and preparation.
• Advising clients on data science opportunities, communicating complex findings clearly, contributing to repeatable solution frameworks, and fostering cross‑functional collaboration with engineering, strategy, and client services teams.

You will join a team of Data Scientists and Analysts who are passionate about turning data into business impact. You'll work closely with our Data Engineering team that builds the pipelines and infrastructure that power your models—and at times, you'll contribute directly to that work.

Beyond your immediate team, you will collaborate with stakeholders across our organization (strategy leads, account directors, creative teams) and directly with client marketing and analytics teams.

You are a practical, business‑minded data scientist who prioritizes delivering value over theoretical perfection. You have strong statistical intuition and can clearly explain analytical approaches and their limitations to both technical and non‑technical audiences. You're not just a notebook data scientist—you understand what it takes to get models into production.

• Solid statistical foundation:
  Strong understanding of inferential statistics, hypothesis testing, regression analysis, and experimental design. You don't need to derive algorithms from scratch, but you must understand when and why to apply them.
• Applied machine learning experience:
  Hands‑on experience building propensity models, lookalike/similarity models, customer segmentation, churn prediction, lifetime value models, and recommendation systems.
• Proficiency in Python and SQL for data manipulation, analysis, and model development.
• Production‑aware mindset:
  Understanding of what it takes to move models from experimentation to production. You're comfortable working with Data Engineers on deployment, and familiar with concepts like scoring pipelines, feature engineering workflows, and orchestration tools (e.g., Airflow, Terraform).
• Good engineering practices:
  Comfort with version control (Git), writing clean and maintainable code, and collaborating in shared codebases.
• Experience with cloud ML platforms:
  Familiarity with cloud‑based ML services (e.g., GCP Vertex AI) and/or marketing platform ML capabilities (Salesforce Einstein, Adobe Sensei).
• Data exploration skills:
  Ability to use visualization tools to inspect data, validate assumptions, and inform modeling decisions.
• Business acumen:
  Ability to connect analytical work to business outcomes and communicate value in terms clients care about. Agency or consulting experience is a strong advantage.
• Collaborative mindset:
  Comfort working in cross‑functional teams and partnering closely with engineers, strategists, and client stakeholders.

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