Senior Data Scientist

New

SwiftlyRetail media analytics

Listing location: Argentina; Workplace type: Remote; Employment type: Full Time Contractor (Remote - Offshore)ContractSenior

Salary7,000 - 8,000 USD per month

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Job Details

Experience: 5+ years of professional experience across data science, applied machine learning, and data engineering
Required Skills: PythonSQLMachine LearningSparkDatabricks

Bring 5+ years of professional experience across data science, applied machine learning, and data engineering.
Have strong Python, SQL, and Spark skills, including hands-on experience with Databricks.
Have hands-on experience developing marketing or advertising models, such as campaign measurement, marketing mix modeling, attribution, or audience, propensity, and uplift modeling.
Have experience building, deploying, and monitoring predictive and machine learning models in production.
Have a strong foundation in statistics and experimental design, including hypothesis testing, regression, and causal inference.
Understand data modeling, pipelines, and ETL processes, with hands-on experience building views, tables, and transformations for machine learning and analytics.
Hold a B.S. in Mathematics, Statistics, Computer Science, Computer Engineering, or a related field.
Have operational experience with large-scale data warehouses and/or data lakes.
Be able to collaborate with data analysts, data engineers, and internal stakeholders in an ambiguous, fast-paced environment.
Be able to leverage AI tools in day-to-day work.
Preferred: experience in marketing, retail, CPG, e-commerce, or retail media/ad tech.
Preferred: experience with A/B tests, holdout and geo experiments, incrementality studies, Databricks capabilities, LLM/embedding/NLP approaches, or data visualization and monitoring tools such as Grafana, Databricks SQL dashboards, or Tableau.

Build, validate, and A/B test predictive models for marketing and shopper outcomes.
Develop campaign measurement approaches, including ROAS/iROAS, incrementality and holdout testing, and marketing mix modeling, and communicate recommendations to business stakeholders.
Own end-to-end data science projects, from data sourcing and feature engineering through model development and production pipelines.
Source and process large-scale, multi-tenant retail data using Python, Spark, and SQL in Databricks; write SQL and create views and tables.
Build and maintain upstream pipelines and well-modeled datasets, partnering with analytics and data engineering teams on data contracts, quality expectations, and shared metric definitions.
Implement testing, data observability, and monitoring for data quality, model performance, and drift.
Create dashboards and alerts for model, pipeline, and data monitoring.
Partner with data analysts and data engineers to answer data questions, automate repeatable processes, and help ensure report accuracy.
Identify failure points and resolve complex data and pipeline issues; participate in a rotating on-call schedule.
Use AI tools such as Claude in day-to-day work.

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