Head of Engineering, Applied AI

Theta

. Own Theta’s engineering and lead the data science team, with most time spent building .

Posted 10/20/2026full-timeRemote • United StatesLeadWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in leading data science teams and engineering, with a strong focus on building and deploying AI-assisted workflows on cloud infrastructure. Proficient in managing production data systems and ensuring reliable execution from data preparation to model outputs.

Highest-signal resume keywords

Cloud Infrastructure ExperienceProduction Data Systems ManagementAI-Assisted Workflow DevelopmentTeam Leadership in Data ScienceCritical Evaluation of Data Outputs

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data PreparationModel ExecutionStatistical ModelingR ProgrammingInfrastructure as CodeCI/CDTerraformMachine Learning SystemsModel DeploymentData Validation

Soft Skills

Problem SolvingTechnical GuidanceClient CommunicationCuriosityJudgment

Tools & Technologies

GCPAWSAzureDatabricksSnowflakeRcppSpark

Industry Keywords

AI-Assisted WorkflowsProduction StackModel PackagingDependency ManagementClient Delivery

Tech Stack

Tools & technologies

AWSAzureCloudGoogle Cloud PlatformSparkTerraformC++

About the role

Key responsibilities & impact

Own Theta’s engineering and lead the data science team, with most time spent building
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 curiosity and judgment to work across engineering, data science, and client delivery
Built and operated production data or ML systems end to end at scale on cloud infrastructure
Cloud infrastructure experience; GCP preferred, with 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
Critical evaluation of data and outputs, including knowing which issues should stop a run
Willingness to learn and work in the 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 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, learning unfamiliar tools quickly, and critically evaluating their 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