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Senior Data Scientist || Minneapolis, MN (Hybrid)

Hybrid in Minneapolis, MN, US • Posted 3 hours ago • Updated 3 hours ago

Contract Independent

Contract Corp To Corp

Contract W2

12 Months

No Travel Required

Hybrid

Depends on Experience

Verito Solutions

Fitment

Dice Job Match Score™

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Job Details

Skills

Data Scientist

Summary

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

Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.

Dice Id: 91170457
Position Id: 20590-40870-1785350624
Posted 3 hours ago

Company Info

About Verito Solutions

At Verito Solutions, our core mission is to be an essential partner in our clients’ success. With a strong vision to become a global leader in delivering innovative and value-driven technology solutions, we are committed to exceeding expectations at every step. Our team is fueled by passion, expertise, and an unwavering determination to provide cutting-edge solutions tailored to the evolving needs of businesses.

We understand the challenges organizations face in today’s fast-paced digital landscape. That’s why we focus on delivering technology solutions that not only enhance efficiency but also save our clients valuable time, money, and effort. Whether it’s optimizing workflows, strengthening cybersecurity, or driving digital transformation, Verito Solutions is dedicated to empowering businesses with seamless, scalable, and future-ready technology.

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Hybrid in Minneapolis, Minnesota

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Data Engineer with AI/LLM infrastructure Minneapolis, MN (Hybrid) Phone + Video Job description: AI systems are only as good as the data feeding them. This role owns the infrastructure that gets data from internal systems, document stores, APIs, and enterprise databases into vector indexes, knowledge bases, and structured stores that AI agents can reliably query. You'll build ingestion pipelines with freshness management, design chunking and embedding strategies, and ensure retrieval qual

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