Data Science Engineer

The Planet Group; Cupertino (CA); Remote; USD 103,000 - 110,000; Full time

Job summary

The Planet Group is seeking a Data Science Engineer for an 18-month contract, offering a remote schedule within 100 miles of Culver City or Cupertino, CA. The role focuses on marketing ML modelling, Gen AI components, and end-to-end ML delivery with Airflow pipelines.

Strong Python skills, statistics knowledge, and experience with data warehouses like Redshift are required. Collaboration with analytics SMEs is important, as is the ability to own the full model lifecycle from concept to

Qualifications

• Proven experience building and deploying ML models for marketing use cases, including time-series forecasting and media-mix style modelling.
• Strong Python skills and grounding in statistics and experimentation, including backtesting and model validation.
• Hands-on experience building and operating batch ML pipelines in Apache Airflow with data quality checks.
• Experience with Gen AI components: MCP servers, LLM agents, tool-calling workflows and evaluation frameworks.
• Working knowledge of SQL and data warehouses (e.g., Redshift).
• Ability to explain model assumptions and results to technical and non-technical stakeholders.

Responsibilities

• Design, build and productionize ML and Gen AI components for marketing planning and estimation.
• Develop forecasting, estimation and marketing response models, including budget-allocation recommendations.
• Build Gen AI components with MCP servers, agents, retrieval and entity resolution, with evaluation frameworks.
• Work independently from concept to production, including feature engineering, model deployment in Python services and Airflow pipelines.
• Collaborate with analytics SMEs to shape modelling approaches and validate results.
• Be a primary ML engineering contact during PST business hours.

Skills

Marketing ML modelling

Python programming & statistics

End-to-end ML delivery

Gen AI components

SQL & data warehousing

Communication & collaboration

Forecasting & modelling concepts

Campaign estimation

Tools

Python (libraries: pandas, NumPy, scikit-learn)

Apache Airflow

Redshift or data warehouse

Prophet / PyMC / Stan

Pandas / NumPy / scikit-learn

LLM tooling & evaluation frameworks

Vector databases & embeddings

Job description

Data Science Engineer
Location:Remote within 100 miles of Culver City or Cupertino, CA
Schedule: 40 hours
Duration:18months
Pay:$75/hr-$80/hrDOE

The Planet Group is looking for a Data Science Engineer to join our well-known Fortune 500 client on a 18-month contract, working a remote schedule.

Data Science Engineer | Qualifications

• Marketing ML modelling:Proven experience building and deploying ML models for marketing use cases, includingtime-series forecasting, effort/resource estimation, and media-mix style response curve and diminishing-returns(saturation) modelling. Strong understanding of marketing performance concepts such as budget allocation, marginalefficiency, pacing and incrementality.
• Python and statistics: Strong Python skills (pandas, NumPy, scikit-learn, statsmodels, and a forecasting orprobabilistic modelling library such as Prophet, PyMC or Stan). Sound grounding in statistics and experimentation,including uncertainty quantification, backtesting and model validation.
• End-to-end ML delivery: Hands-on experience building and operating batch ML pipelines in Apache Airflow(scheduling, backfills, data quality checks, retraining). Demonstrated ability to own the full model lifecycle independently,from concept to production, including feature engineering, model versioning, drift monitoring and retraining feedbackloops.
• Gen AI components: Good understanding of developing Gen AI components, including:
  • MCP servers and tool design, with a governed semantic layer for natural-language data access
  • LLM agents, tool-calling workflows, prompt engineering and structured output
  • LLM evaluation frameworks (golden datasets, LLM-as-judge, regression testing)
  • Vector databases and embeddings for RAG, and entity resolution across systems
• Data foundations: Working knowledge of SQL and data warehouses (e.g., Redshift).
• Communication and collaboration: Ability to explain model assumptions, trade-offs and results to technical and non-technical stakeholders, and to work effectively with analytics SMEs.

Preferred Qualifications:

• Experience developing services and APIs in Python
• PySpark for large-scale feature processing
• Bayesian modelling
• Optimization and constrained budget allocation techniques

Data Science Engineer | Responsibilities

• Design, build and productionize the machine learning and Gen AI components behindMarketing's media planning, campaign estimation and intelligence products.
• Develop forecasting, estimation andmarketing response models, including budget-allocation recommendations built on response curves and diminishing-returnsanalysis.
• Build Gen AI components, including MCP servers, agents, retrieval and entity resolution, with evaluationframeworks to keep LLM output reliable and consistent.
• Work independently, taking models from concept through to production. That covers problem framing, featureengineering, model development, validation, deployment as Python services and Airflow pipelines, and ongoing monitoringand retraining.
• Collaborate with internal analytics SMEs to shape modelling approaches and validate results.
• Be a primary point of contact for ML engineering support during business hours (PST).