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# Lead Data Scientist

- **Company:** CLOUD SOLUTIONS TECHNOLOGY CORPORATION
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** A/B Testing, Artificial Intelligence, Computer Vision, Code Review, Continuous Integration, Distributed Computing Environment, Python (Programming Language), Machine Learning, Natural Language Processing, Recommender Systems, Azure Machine Learning, SQL Databases, Feature Engineering, Large Language Models, Snowflake, Apache Spark, Deep Learning, Model Validation, Xgboost, Machine Learning Operations, Drift Detection, Databricks
- **Published:** October 9, 2026
- **Apply:** https://arc.dev/remote-jobs/j/redirect/ppzzd5gt3k

## About the Role

* 10-12 years of experience in data science, with demonstrated ownership of models from problem

definition through production impact and measured business outcome * Deep, defensible grounding

across model families: classical ML (regression, tree ensembles like XGBoost/LightGBM) and

deep learning (architecture choice, training at scale), with clear judgment on when each is the right

call

 * Domain depth in at least one deep learning area relevant to enterprise work: NLP, forecasting,

recommendation systems, or computer vision, snowflake including hands-on architecture and

training decisions, not just fine-tuning a pretrained model

 * Production-grade feature engineering and training infrastructure experience, including experience

with distributed training or large-scale compute (Spark, Ray, or equivalent)

 * Experience owning a model in production long-term: monitoring, drift detection, retraining triggers,

rollback

 * Experience leading a team technically, including mentoring and setting the standard for others'

modeling and code work

 * Strong client-facing communication, able to hold a technical argument with a client stakeholder

and explain a model's limitations plainly

 * cloud/ML platform stack - Databricks, SageMaker, Vertex AI, Azure ML, etc.

 * GenAI/LLM applied experience: retrieval design, evaluation harnesses, honesty about failure

modes, not just demo projects

 * MLOps tooling depth: MLflow or similar model registries, automated retraining pipelines, CI/CD

for ML

 * Prior consulting or professional services background, comfortable across multiple concurrent client

engagements

Primary Skills

 * Classical ML - regression, classification, tree ensembles (XGBoost/LightGBM), model selection

judgment

 * Snowflake - Should be genuine hands-on experience working on Snowflake Platform.

 * Deep learning - architecture design and training at scale in at least one domain area (NLP,

forecasting, recommendation systems, or computer vision)

 * Causal inference / experimentation - A/B testing, uplift modeling, confounderaware analysis

 * Feature engineering & training infrastructure - production-scale pipelines, distributed

compute (Spark, Ray, or equivalent)

 * MLOps / production ownership - deployment, drift and degradation monitoring, retraining

triggers, rollback

 * Technical leadership - mentoring, code/model review standards, setting team technical bars.

 * Client communication - defending model tradeoffs and limitations to nontechnical stakeholders

 * Python and SQL - production-grade

## Description

We're looking for a Lead Data Scientist who sets technical direction for a workstream, goes deep on the

modeling and architecture decisions personally, and leads a small team of data scientists/engineers. You'll

be the senior technical authority the client turns to when a model decision needs defending.

What you'll do

 * Own the full model lifecycle end to end: problem framing, feature engineering, model architecture

selection, training at scale, validation, deployment, and post-launch monitoring and retraining

 * Make and defend architecture-level calls: which model family, how much complexity is actually

justified by the data and the business case, when a classical model beats a deep learning one and

vice versa

 * Design and run rigorous experiments (A/B tests, causal inference, uplift modeling) and be able to

explain confounders and why an offline metric lied to you

 * Build and own feature pipelines and training infrastructure that hold up at production scale and

under data drift

 * Diagnose model degradation in production and make the retrain-versus-redesign call, including

rollback plans

 * Set the technical bar for the team: code review standards, experiment tracking, model validation

rigor, and mentor 2-4 data scientists/engineers against it

 * Be the primary technical point of contact for the client, translating ambiguous problems into scoped