Job Url: https://jobs.ashbyhq.com/chalice/e35b6295-1b74-4348-8b50-57b77194ba11

Job Description: Forward Deployed Data Scientist
Location
Hybrid/Remote

Employment Type
Full time

Location Type
Remote

Department
Data Science

Compensation
Compensation Range $150K – $180K
Overview
Application
About Chalice

Chalice Custom Algorithms (chalice.ai) is the leading AI application for brands applying their own data and analytics in the real-time decisioning of ad buys. Chalice’s software automates data ingestion, predictive analytics, and deployment of custom bidding instructions in a supervised learning environment, where advertisers can visualize and test their custom algorithms. Advertisers’ algorithms can be deployed in all major DSPs, as well as Meta and YouTube. Chalice was named “Best Demand Side Tech” by AdExchanger, and powered AdWeek’s “Best Use of Programmatic” in the 2023 Media Plan of the Year awards.

About The Role

We are seeking a forward deployed Data Scientist to join our cross-functional Data
Science team. This role is uniquely positioned at the intersection of media analytics,
product innovation, and applied machine learning. You will work directly with high -value
clients and internal stakeholders to extract insights from media performance, recommend
improvements to DS/ML infrastructure, and actively configure and deploy models across
new or evolving use cases.

This is not a traditional analytics position—ideal candidates will thrive in ambiguity,
prototype quickly, and contribute to core data science products by bridging product -
market context with modeling expertise.

Key Responsibilities

Client-Facing Media Analytics & Strategy

Serve as the embedded data science expert across client teams to analyze media performance, model performance, and recommend and/or prototype improvements.

Translate raw data, model logs, and client KPIs into actionable media recommendations.

Conduct lift analyses, segment-level attribution, and targeting strategy refinement.

Core Model Configuration & Deployment

Build and adapt MLE and DS configurations for existing modeling products (e.g., audience scoring, incremental lift, bid modifiers).

Configure pipelines, stratified sampling logic, or feature transformations tailored to client-specific challenges.

Work closely with engineers and product managers to productionize prototypes into scalable tools.

Product Feedback & Internal Innovation

Act as a conduit between clients and the core DS team to surface edge cases, performance drift, and new feature requests.

Suggest improvements to model interpretability, automation, and monitoring frameworks based on field deployment.

Participate in research and experimentation cycles around contextual targeting, identity resolution, and model evaluation.

Qualifications

5+ years in data science, analytics, or machine learning roles, preferably within
advertising, martech, or SaaS environments. Bachelor’s degree required, MS
preferred.

Strong grasp of causal inference, media measurement, and experimentation (e.g.,
geo holdouts, diff-in-diff, matched markets).

Experience building data pipelines and models in Python (bonus for familiarity with
Spark, Airflow, or MLflow).

Ability to communicate complex technical concepts clearly to non-technical
stakeholders.

Entrepreneurial mindset with the confidence to own ambiguous, high-impact
initiatives from start to finish.

Requires working knowledge of digital ad campaign workflow and customer service