Top Data Science Jobs in Charleston, SC

Design, build, and deploy ML models across diverse use cases; work with managed cloud ML platforms and containerized solutions; collaborate with engineers on production integration and MLOps best practices; preprocess and analyze large datasets; mentor teammates and influence technical direction; translate complex ML concepts for technical and non-technical stakeholders.

Design, develop, and deploy causal inference and predictive models (uplift, synthetic control, DML) for customer loyalty, churn, and LTV. Build foundational data models and scalable features, perform large-scale SQL ETL, operationalize models in cloud, partner with stakeholders, and present insights to drive revenue and loyalty program optimization.

The role involves building and validating Marketing Mix Models and geo-experiments, managing measurement infrastructure, and communicating insights to stakeholders.

Design, develop, and deploy time-series and machine-learning forecasting models (ARIMA, SARIMA, ETS, XGBoost, LightGBM) for demand planning, inventory, and sales forecasting. Perform large-scale SQL data processing, operationalize models in cloud environments (Azure preferred), present insights, and lead client workshops.

As a Lead Data Scientist, you'll design experiments, build predictive models, and lead a team while shaping data-driven strategies to enhance AI deployments.

The Principal Data Scientist Consultant will develop and maintain clinical datasets using R, ensure CDISC compliance, generate reports, and contribute to internal tools while collaborating with clinical teams.

As a Senior Data Scientist, you'll lead projects leveraging data to enhance healthcare outcomes, mentor team members, and align data efforts with business goals.

As a Senior Data Scientist, you will develop statistical models and machine learning algorithms to improve patient enrollment and trial management while ensuring compliance in a healthcare data environment.

As a Senior Data Scientist, you will design and deploy machine learning and graph algorithms to enhance identity trust solutions, own end-to-end projects, and collaborate with product and engineering teams to improve KYC and compliance products.

Lead the design and deployment of machine learning and graph-based systems for international identity verification, ensuring compliance and addressing regional challenges.

The Senior Data Scientist in Conversational AI & NLU designs and optimizes AI solutions, evaluates models, collaborates with stakeholders, and analyzes conversational data to improve user experience.

Lead the development and deployment of advanced ML/AI products, mentoring teams and collaborating with stakeholders to influence business outcomes.

The Senior Data Scientist at Fetch will develop and scale data science capabilities, architect predictive models, and influence product strategy through collaboration and experimentation. They will lead initiatives focused on engagement, churn, and business impact measurement using advanced analytics and machine learning tools.

The Senior Data Scientist will develop machine learning models, conduct A/B tests, and work with behavioral data to enhance customer engagement and partner ROI.

Lead data science for Yahoo Mail subscriptions: analyze acquisition, conversion, pricing, retention, and churn; design and analyze A/B and multivariate experiments; build predictive and clustering models; partner with product, engineering, design, and business teams to deliver data-driven growth and monetization insights.

Lead development and operationalization of measurement products (MMM, incrementality, insights, recommendations). Build, deploy, and maintain production-grade statistical and ML systems, debug end-to-end data and model issues, evaluate methodologies, and partner with engineering, product, and customer teams to improve measurement accuracy and reliability. Provide customer-facing explanations and support for measurement outputs.

Build and deploy credit and fraud risk models using advanced ML/statistical methods. Partner with stakeholders, extract insights from large transaction datasets, automate feature/production pipelines, mentor junior data scientists, and ensure model quality and deployment scalability.

Proactively explore RevenueCat's data to identify product opportunities, translate ambiguous problems into analyses and production-grade predictive/descriptive models, partner with Product and Engineering to ship data-powered features (LTV prediction, experimentation, benchmarking), define statistical approaches, iterate on models in production, and communicate insights to technical and non-technical audiences.