Applied Data Scientist

Cresta Cresta · Vertical AI · United States · Remote · Product

Applied Data Scientist role focused on developing and shipping new AI-powered features for enterprise contact centers, working directly with customers to understand needs, iterate on solutions, and generalize learnings into productized capabilities. The role involves building LLM-based systems, curating evaluation guidelines and datasets, and staying current with AI/ML research.

What you'd actually do

  1. Co-develop new capabilities with a small number of high-impact enterprise customers along with our product, engineering, and design teams; using their real workflows and constraints as your testbed
  2. Plan and run short, focused design-partner engagements (days to weeks) where you ship early versions, collect structured feedback, and iterate quickly
  3. Generalize learnings from each design partner into reusable, productized capabilities rather than one-off bespoke models
  4. Partner with domain experts to curate high-quality eval guidelines and datasets for domains such as CSAT prediction and outcome prediction (across both human<>human and human<>AI interactions)
  5. Stay close to the research frontier in ML/AI, LLMs, and evals, translating promising ideas into pragmatic, shippable improvements

Skills

Required

  • 3+ years building and shipping models for real-world business applications, ideally in NLP and LLM-based systems
  • Strong proficiency in Python and standard ML / data tooling (e.g., SQL, data pipelines, experiment frameworks)
  • World-class first principles thinking and ML intuition
  • Ability to turn ambiguous product asks into crisp problem statements, eval specs, metrics, and hypotheses
  • Experience working directly with customers or internal stakeholders to understand constraints, explain tradeoffs, and iterate on solutions
  • Comfort working with design-partner style engagements where requirements evolve rapidly and you’re expected to co-create the solution
  • Track record of building evaluation suites that go beyond single scalar metrics to capture reliability, safety, and qualitative user experience
  • Strong written and verbal communication skills; able to clearly explain complex technical work to both engineers and non-technical partners

Nice to have

  • Experience building ML or analytics systems for customer experience, contact centers, or enterprise SaaS
  • Prior work on analytics / insights products (e.g., deep research systems, anomaly detection, customer experience analytics, voice of customer)
  • Published work at ML / NLP conferences

What the JD emphasized

  • building and shipping models for real-world business applications
  • design-partner style engagements
  • Track record of building evaluation suites that go beyond single scalar metrics to capture reliability, safety, and qualitative user experience

Other signals

  • customer-facing AI features
  • LLM applications
  • evaluating AI models