> Markdown version of [/jobs/ext/1018263-data-scientist](https://www.wearedevelopers.com/jobs/ext/1018263-data-scientist). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Scientist - **Company:** ClearCompany - **Location:** United States (Remote available) - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Airflow, Amazon Web Services, Data Analysis, Artificial Neural Networks, Microsoft Azure, Software as a Service, Cloud Computing, Code Review, Continuous Integration, Customer Data Management, Extract Transform Load (ETL), Statistical Hypothesis Testing, Python (Programming Language), Machine Learning, Standard Sql, Google Cloud, Feature Engineering, Large Language Models, Snowflake, Apache Spark, Deep Learning, Pandas, Kubernetes, Information Technology, Xgboost, Machine Learning Operations - **Published:** June 30, 2026 - **Apply:** http://teamvelocitymarketing.hrmdirect.com/employment/job-opening.php?req=3749439&req_loc=1374574&& ## About the Role * Bachelor's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or related field, Master's or PhD preferred * 5+ years of experience building and deploying production ML systems, with a track record of measurable business impact * Strong communication and business problem-solving skills Technical Skills * Expert Python and SQL * Machine Learning: XGBoost, LightGBM, Random Forests, Neural Networks, Deep Learning * Statistics: Regression, Bayesian methods, hypothesis testing, experimental design, time series, causal inference * Snowflake, Matillion, dbt, Pandas, Spark, Airflow * Cloud: Google Cloud (preferred), AWS, or Azure * MLOps: MLflow, Kubeflow, Vertex AI Pipelines, Feature Stores, CI/CD * Data quality and observability: Great Expectations, Monte Carlo, or similar frameworks * LLMs and AI: OpenAI, Gemini, Claude, LangChain, LangGraph, Semantic Kernel, RAG, vector databases Preferred Experience * Large-scale customer data platforms * Marketing analytics and personalization * Automotive or SaaS industry experience * Real-time inference and streaming platforms Success Metrics * Deliver production-ready ML models with measurable business impact * Improve prediction accuracy and operational efficiency * Implement model monitoring and retraining * Mentor team members and establish Data Science best practices * Contribute to team capability growth through documentation, code review standards, and mentorship outcomes that raise the overall quality of the Data Science function ## Description * Design, build, train, evaluate, and deploy machine learning models. * Develop predictive models including churn, propensity, lead scoring, customer lifetime value, recommendation engines, forecasting, and marketing attribution. * Perform statistical analysis, hypothesis testing, causal inference, and A/B test analysis. * Build feature engineering and model training pipelines. * Deploy and monitor production ML models, including model drift detection and retraining. * Collaborate with Product, Engineering, Analytics, and executive leadership. * Mentor junior data scientists and establish best practices. ## Related Videos - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [TikTok's Privacy Innovation](https://www.wearedevelopers.com/videos/1036-tiktok-s-privacy-innovation) - [Advanced Typing in TypeScript](https://www.wearedevelopers.com/videos/496-advanced-typing-in-typescript) - [Data Science on Software Data](https://www.wearedevelopers.com/videos/162-data-science-on-software-data) - [Explainable machine learning explained](https://www.wearedevelopers.com/videos/589-explainable-machine-learning-explained) ## Related Articles - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [Top Big Data Technologies That You Need to Know](https://www.wearedevelopers.com/magazine/108-top-big-data-technologies-that-you-need-to-know) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [Data Engineer Salary UK](https://www.wearedevelopers.com/magazine/253-data-engineer-salary-uk)