Careers at EcoMetricX

Qualifications

• →Bachelor's degree or higher in Computer Science, Data Engineering, or related field
• →Strong proficiency in Python and SQL
• →Experience designing and maintaining ML/AI data pipelines
• →Familiarity with database design and data visualization tools
• →Experience with AWS cloud services preferred

Qualifications

• →Bachelor's degree or higher in Data Science, Statistics, Computer Science, or related field
• →Proficiency in Python and SQL
• →Experience with data quality assessment, validation, and cleaning
• →Familiarity with cloud data platforms (AWS preferred)
• →Meticulous attention to detail and strong analytical skills

Qualifications

• →Advanced quantitative degree (Economics, Statistics, Computer Science, or related)
• →Proficiency in Python and SQL
• →Experience with causal inference, machine learning, or econometric methods
• →Ability to explore complex customer datasets and estimate causal relationships
• →Strong written and oral communication skills

Qualifications

• →Advanced degree in Statistics, Economics, Computer Science, or related quantitative field
• →Proficiency in Python (PyTorch, TensorFlow, or similar ML frameworks)
• →Strong background in statistical modeling and time-series forecasting
• →Experience with energy sector data preferred
• →Strong communication skills to present results to non-technical stakeholders

Job Description

You'll rotate across two or three concurrent client engagements at any given time. Expect to be involved in things like: • Redesigning ML pipelines that power our evaluation products so they handle larger data volumes with cleaner tenant isolation. • Building secure, auditable CI/CD workflows (GitHub Actions, SOC 2-aligned approvals) so client code changes can ship safely without a bottleneck on one person. • Hardening authentication and data-handling paths. We've been tightening token handling, encryption-at-rest, and role-based access in response to client compliance needs (SOC 2, GDPR, CPUC, HIPAA-adjacent work). • Integrating LLM features into customer-facing products: retrieval pipelines, hallucination guardrails, token-cost optimization, and multi-tenant chatbot orchestration. • Owning API performance work. We solve annoying problems around SQL query shape, concurrency limits, and ingestion from third-party data providers and partner APIs. • Building ingestion and transformation layers that bend to evolving client logic models, not the other way around. • Helping us produce technically rigorous RFP responses: reading a scope, spotting the risk, and sketching the architecture before a line of code is written.

Qualifications

• →Roughly 3 to 6 years of professional software engineering experience, ideally
• →with at least one role where you were the person responsible for a system
• →working in production.
• →Strong working knowledge of Python (our primary language) and comfort with
• →SQL.
• →Experience designing and deploying on a major cloud platform. We mostly live in
• →AWS (including managed ML, object storage, serverless compute, and IAM) with
• →some lakehouse and Google Cloud Storage work.
• →Practical experience with API design, data pipelines, and the tradeoffs between
• →batch and streaming.
• →Familiarity with CI/CD, version control workflows (GitHub Actions), and the basics
• →of infrastructure-as-code.
• →A serious respect for security and data privacy. You should understand why
• →unencrypted tokens are a problem, what least-privilege means, and how to
• →reason about PII.

Preferred Qualifications

• →Experience with LLM applications in production: RAG, prompt orchestration,
• →evals, cost and latency tradeoffs, and guardrails.
• →Exposure to multi-tenant architecture (row-level security, tenant isolation, per-
• →tenant cost attribution).
• →Comfort reading a statistical model someone else wrote and implementing it
• →cleanly: causal inference, Bayesian shrinkage, time-series forecasting, or similar.
• →You don't need to be a statistician, but you shouldn't run away from one.
• →Work touching regulated data: HIPAA, GDPR, SOC 2, or utility data
• →confidentiality.
• →Any direct experience with energy, utilities, metering data, or geospatial
• →analytics.