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Senior Data Scientist || Minneapolis, MN (Hybrid)

Verito SolutionsMinneapolis, MN🇺🇸United StatesPosted 29 Jul 2026

Quick Overview

Work Type
Hybrid
Level
Mid Senior

Job Description

Senior Data Scientist
Minneapolis, MN (Hybrid) 
Phone + Video
Job description: 
 
WHAT YOU'LL DO
  • ▸  Frame ambiguous business problems into well-defined ML and AI problem statements with measurable success criteria
  • ▸  Own end-to-end model development — feature engineering, training, evaluation, and production handoff
  • ▸  Build and evaluate LLM-augmented workflows — combining classical ML signals with generative AI where appropriate
  • ▸  Design and maintain offline and online evaluation frameworks — ensuring model quality before and after deployment
  • ▸  Partner with MLOps to instrument models with drift detection, monitoring, and retraining triggers
  • ▸  Communicate findings clearly to both technical and non-technical stakeholders including business and risk teams
REQUIRED EXPERIENCE
  • ▸ master’s in computer science or PHD in Computer Science with Specialization in Data Science, Mathematics & Statistics.
  • ▸ 10+ years total IT experience with 3+ years building and deploying ML models in production environments — not just notebooks
  • ▸  Strong statistical foundation — hypothesis testing, regression, classification, time series, causal inference
  • ▸  Production Python — scikit-learn, PyTorch or TensorFlow, Pandas, NumPy, clean modular code
  • ▸  Hands-on feature engineering and feature store patterns for structured and unstructured data
  • ▸  Experience with model evaluation rigor — holdout sets, cross-validation, leakage prevention, business metric alignment
  • ▸  Worked in a regulated or compliance-sensitive environment — model documentation, auditability, and explainability requirements
  • ▸  Can distinguish when a problem needs ML vs. a simpler rule-based approach — avoids over-engineering
AI & LLM EXPECTATIONS
  • ▸  Practical understanding of LLM capabilities and limitations — knows when to use generative AI vs. classical ML vs. deterministic rules
  • ▸  Experience building or evaluating RAG pipelines or LLM-augmented analytics workflows — even if not the primary architect
  • ▸  Familiarity with LLM evaluation frameworks — RAGAS, DeepEval, LLM-as-judge, or equivalent golden dataset approaches
  • ▸  Understands hallucination risks and validation strategies for LLM outputs used in business-critical decisions
  • ▸  Comfortable working within an enterprise LLM gateway environment — model routing, cost awareness, token management
NICE TO HAVE
  • ▸  Financial services domain — wealth management, portfolio analytics, risk scoring, client segmentation, or fraud detection experience
  • ▸  Experience with NLP pipelines for financial document understanding, summarization, or entity extraction
  • ▸  Familiarity with A/B testing and causal inference for evaluating model interventions in production
  • ▸  Databricks or Snowflake ML for large-scale feature computation and model training
  • ▸  Exposure to graph-based analytics or network analysis for relationship modeling
  • ▸  MLflow, Weights & Biases, or equivalent for experiment tracking and model registry
TECH STACK
  Python · scikit-learn · PyTorch      Pandas · NumPy · SciPy      Feature Engineering · Feature Stores      Classification · Regression · Time Series      NLP · Text Analytics · spaCy      LLM Integration · RAG Pipelines      RAGAS · DeepEval · LLM Evaluation      MLflow · Weights & Biases      AWS SageMaker · Azure ML · Bedrock      Databricks · Snowflake      Spark · PySpark      PostgreSQL · SQL · BigQuery      A/B Testing · Causal Inference      Model Monitoring · Drift Detection      Docker · Kubernetes      FastAPI · REST APIs      CI/CD · GitHub Actions      Explainability · SHAP · LIME 
 
 
(“ Believe you can and you’re halfway there. ”)
 –  Theodore Roosevelt
Yogesh Sharma   |  Lead Tech Recruiter

An -E Verified Company

 
 

Skills

Docker
FastAPI
SQL
AWS
MLOps
MLflow
NLP
NumPy
SciPy
Scikit-learn
Snowflake
Azure
BigQuery
Databricks
Generative AI
GitHub Actions
Kubernetes
LLM
Pandas
PostgreSQL
PyTorch
Python
REST
TensorFlow

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