Head of Engineering, Data Science

Theta

. Own Theta’s engineering function and lead its data science team .

Posted 10/19/2026full-timeRemote • United StatesLeadWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates deep engineering skills and judgment in data science and infrastructure, with a strong focus on building and operating production data or ML systems at scale on cloud platforms. Capable of leading a data science team while ensuring reliable execution and deployment of AI-assisted workflows.

Highest-signal resume keywords

Cloud Infrastructure ExpertiseProduction Data Or ML Systems ExperienceTeam Leadership In Data ScienceAI-Assisted Workflow DevelopmentR-Based Production Stack Familiarity

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data PreparationModel ExecutionStatistical ModelingInfrastructure As CodeCI/CDR ProgrammingC++ Integration Through RcppTerraformDatabricksSnowflake

Soft Skills

Problem SolvingTechnical CommunicationCritical ThinkingHands-On Technical GuidanceClient Engagement

Tools & Technologies

GCPAWSAzureDatabricksSnowflakeSparkRTerraformCI/CD ToolsAI Tools

Industry Keywords

Data ScienceMachine LearningModel DeploymentAI WorkflowsProduction Systems

Tech Stack

Tools & technologies

AWSAzureCloudGoogle Cloud PlatformSparkTerraformC++

About the role

Key responsibilities & impact

Own Theta’s engineering function and lead its data science team
Set technical direction and help the team resolve difficult delivery problems
Work with clients’ technical teams
Architect reliable execution of thousands of models on cloud infrastructure
Work with client engineering teams to package and deploy models in their own environments
Make the pipeline from raw client files to validated model outputs reliable and reusable
Develop AI-assisted workflows for data preparation and modeling
Set standards for testing, validation, and reliable deployment
Review the team’s code and designs
Decide what to build, automate, or outsource while ensuring maintainability
Help data scientists resolve difficult client delivery problems and contribute directly when engagements overlap

Requirements

What you’ll need

Deep engineering skills and judgment across engineering, data science, and infrastructure
Experience building and operating production data or ML systems end to end at scale on cloud infrastructure
Experience with GCP preferred; AWS, Azure, Databricks, or Snowflake also relevant
Experience solving problems across data preparation, model execution, and infrastructure, from messy input files to slow model runs
Ability to critically check data and outputs and identify issues that should stop a run
Willingness to learn and work in an existing R-based production stack
Product engineering mindset, balancing reliability, usability, and delivery needs
Ability to lead a small, experienced data science team through hands-on technical guidance and resolve difficult delivery problems
Comfortable participating in client calls to explain technical methods and trade-offs, discuss data requirements, and understand clients’ technology stacks and infrastructure
Genuine interest in developing AI-assisted workflows, ability to learn unfamiliar tools quickly, and critically evaluate outputs
Preferred: Experience building and evaluating AI-assisted workflows for data preparation and modeling, including LLM or agent-based systems
Preferred: Production R experience, including packaging, dependency management, containers, or C++ integration through Rcpp
Preferred: Statistical modeling, especially familiarity with CLV or BTYD models
Preferred: Experience deploying data or modeling workflows in client-controlled environments, including Databricks, Snowflake, or Spark
Preferred: Infrastructure as code experience, such as Terraform or similar, and CI/CD