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Theta
Theta
11 – 50 Angajați
Sponsor pentru viza H1BConsultanțăFinanțeMarketing
Theta dezvoltă analize predictive ale clienților și metode de evaluare corporativă pentru firme de investiții și corporații. Modelul său CLV Ultra ajută echipele să prevadă comportamentul clienților și să evalueze economia unitară a clienților, susținând deciziile de marketing, strategiile de implicare a clienților și evaluarea afacerilor.
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Head of Engineering, Applied AI

Lead Theta’s engineering and data science teams as they build predictive customer value analytics systems. Guide cloud modeling infrastructure, AI-assisted workflows, and delivery for client engagements.

USStatele Unite ale AmericiiRemoteNormă întreagăLiderLimbă Obligatorie: englezăManager de inginerie
AWSAzureC++CloudGoogle Cloud PlatformSparkTerraform

Descriere

  • Lead engineering and the data science team, contributing hands-on to development.
  • Set technical direction and help the team solve complex delivery challenges.
  • Collaborate with clients’ technical teams.
  • Design cloud infrastructure to run thousands of models reliably.
  • Partner with client engineers to package and deploy models in their environments.
  • Build a reliable, reusable pipeline from raw client files to validated model outputs.
  • Create AI-assisted workflows for data preparation and modeling.
  • Establish standards for testing, validation, and dependable deployment.
  • Review team code and technical designs.
  • Choose what to build, automate, or outsource while maintaining long-term maintainability.
  • Help data scientists address challenging client delivery problems and contribute directly when engagements overlap.

Cerințe

  • Bring deep engineering expertise, curiosity, and sound judgment across engineering, data science, and client delivery.
  • Have built and operated production data or machine learning systems end to end at scale on cloud infrastructure.
  • Bring cloud infrastructure experience; Google Cloud is preferred, with AWS, Azure, Databricks, or Snowflake also relevant.
  • Be able to solve problems spanning data preparation, model execution, and infrastructure, from messy input files to slow model runs.
  • Assess data and outputs critically, including knowing when an issue should halt a run.
  • Be willing to learn and work within the existing R-based production stack.
  • Apply a product engineering mindset that balances reliability, usability, and delivery needs.
  • Lead a small, experienced data science team through hands-on technical guidance and support with difficult delivery problems.
  • Be comfortable joining client calls to explain technical methods and trade-offs, discuss data requirements, and understand clients’ technology stacks and infrastructure.
  • Show a strong interest in developing AI-assisted workflows, learning unfamiliar tools quickly, and critically assessing 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 experience, 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 tools, and CI/CD.

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Deschide