Senior Data Scientist

This is a fully remote position, open to applicants in Argentina.

📋 Description

• Develop, validate, and conduct A/B testing of predictive models that enhance marketing and shopper outcomes.

• Create measurement strategies for campaigns, including ROAS/iROAS, incrementality and holdout testing, as well as marketing mix modeling.

• Convert measurement findings into straightforward recommendations for business stakeholders.

• Manage data science projects from inception to completion, including data sourcing, feature engineering, model development, and implementing production machine learning pipelines.

• Extract and process extensive multi-tenant retail data utilizing Python, Spark, SQL, and Databricks.

• Write SQL queries and establish views and tables.

• Build and sustain upstream data pipelines and well-structured datasets.

• Set data contracts, quality standards, and shared metric definitions in collaboration with analytics and data engineering teams.

• Implement testing, data observability, and monitoring throughout machine learning and data pipelines.

• Oversee data quality, model performance, and drift.

• Develop dashboards and alerts for monitoring models, pipelines, and data.

• Collaborate with Data Analysts and Data Engineers to address data inquiries and enhance report accuracy.

• Identify points of failure and resolve intricate data and pipeline challenges.

• Utilize AI tools such as Claude to boost productivity and quality.

• Participate in a rotating on-call schedule.

• Carry out additional related duties as assigned.

⛳️ Requirements

• Over 5 years of professional experience in data science, applied machine learning, and data engineering.

• Proficient in Python, SQL, and Spark.

• Practical experience with Databricks.

• Familiarity with developing marketing or advertising models, including campaign measurement, ROAS, incrementality, marketing mix modeling, attribution, audience/propensity, or uplift modeling.

• Experience in building, deploying, and monitoring predictive and machine learning models in a production environment.

• Solid grounding in statistics and experimental design, encompassing hypothesis testing, regression, and causal inference.

• Comprehensive understanding of data modeling, data pipelines, and ETL processes.

• Hands-on experience in building views, tables, and transformations that support machine learning models and analytics.

• A Bachelor’s degree in Mathematics, Statistics, Computer Science, Computer Engineering, or a related discipline.

• Operational experience with large-scale data warehouses and/or data lakes.

• Capability to collaborate effectively with data analysts, data engineers, and internal stakeholders.

• Ability to thrive in a dynamic and ambiguous environment.

• Proficiency in leveraging AI tools in daily tasks.

• Skill in creating well-structured, documented, comprehensible, and maintainable data products.

🏝️ Benefits

• Competitive compensation based on experience.

• Contract of record managed through Deel.

• Flexible remote work options.

• Full-time contract employment.