Top Remote Data Science Jobs in San Luis Obispo, CA

Lead data science engagements with customers to acquire, clean, analyze, and model data; develop scalable operational models and evaluation methods; coach engineers on best practices; drive solution strategy, ethics, and industry thought leadership; write extensible code and collaborate across teams to deliver cloud-based AI solutions.

Seeking a skilled Data Scientist/Machine Learning Engineer to develop machine learning models, design AI systems, and collaborate in data analytics. Responsibilities include productionizing models, data engineering, and cross-functional collaboration.

Lead customer-facing data science engagements: acquire and prepare data, evaluate and operationalize models at scale, drive modeling best practices, research industry trends, write extensible code, and define solution and customer strategy while promoting ethics and privacy.

As a Staff Data Scientist, you will analyze large-scale broadband data, develop and validate ML models, and integrate solutions into Calix Cloud, collaborating with teams for enhanced network intelligence and subscriber experience.

Lead development and deployment of scalable ML, prescriptive analytics, and constrained-optimization models for real-time routing, scheduling, capacity allocation, and personalization in cloud gaming. Build data ingestion and processing frameworks, improve data quality, analyze patterns with supervised/unsupervised methods, and collaborate cross-functionally to deliver prescriptive pipelines and actionable recommendations that optimize latency, utilization, and retention.

Lead development and deployment of machine learning models, perform EDA and data engineering on large datasets, train models including LLMs and unstructured-data solutions, build Power BI dashboards, automate processes, present findings to leadership, and mentor junior data scientists while working with SQL, Python, AWS/Azure, Snowflake, and Salesforce.

Design, implement, and maintain marketing inference models (e.g., MMM and attribution). Analyze large datasets and A/B/uplift experiments with rigorous statistics, build predictive and causal models, partner with marketing to generate testable ideas, and communicate insights to drive marketing decisions.

Lead end-to-end delivery of strategic data science and AI projects: define business problems with stakeholders, prepare large datasets, build and deploy predictive, prescriptive, and generative AI models (including LLMs), implement prompt engineering and fine-tuning, ensure production-grade code and ML lifecycle practices, present insights to senior stakeholders, and promote responsible AI and adoption across teams.

Lead product and program analytics for Microsoft skilling experiences. Design funnel and learner-journey analyses, define success metrics, build measurement frameworks, run experiments and causal analyses, develop competency/skill models, partner with product and engineering on telemetry, recommend content and curriculum improvements, mentor data scientists, and ensure data privacy and telemetry quality.

Lead customer-facing data science engagements: manage data acquisition and cleaning, build and evaluate scalable models, drive model best practices and ethics, write extensible code, research industry trends, define solution strategy, and collaborate across engineering and product teams to deliver AI/cloud solutions.

As a Senior Marketing Data Scientist, you will conduct quantitative analyses, manage marketing tests, and provide actionable insights to enhance marketing performance and investment effectiveness.

Lead cross-functional teams to identify and implement revenue-driving strategies. Own revenue optimization, develop quantitative analyses (A/B testing, causal impact), improve data pipelines and tracking, embed AI into workflows, and present insights to leadership to scale and recover revenue.

Seeking a Senior Data Scientist to lead predictive analytics and machine learning initiatives. Responsibilities include building ML models, data management, and mentoring junior data scientists. Must have expertise in AWS and strong statistical knowledge.

Lead end-to-end AI/ML initiatives to improve student retention, engagement, and enrollment. Architect and operate data pipelines, feature stores, and production ML systems; deploy, monitor, and automate models; run experimentation programing and measure business outcomes. Mentor team members and set technical standards across Product, Engineering, and CX.

As a Data Scientist at Sardine, you'll design and deploy data-driven solutions to prevent fraud, collaborating with clients and engineering teams to build and optimize machine learning models.

Senior data analyst/programmer using SAS or R to analyze commercial claims and EHR real-world data. Create longitudinal cohorts, derive baseline/outcome variables, assess data quality, apply epidemiologic study designs and statistical methods (regression, survival), review protocols/SAPs, and program TFLs. Familiarity with OMOP CDM and OHDSI tools is a plus; prior pharmaceutical experience preferred.

Lead design and validation of agentic AI product capabilities for legal workflows using ML, NLP, and LLMs. Develop generative AI approaches (RAG, prompt engineering), hybrid retrieval strategies with embeddings and vector DBs, define agentic workflows, run experiments and evaluations, analyze large legal datasets, produce product recommendations, collaborate cross-functionally, and mentor team members.

Lead data strategy and analytics for cognitive assessment studies: design algorithms and models, implement CDISC-compliant data transfers (SDTM/ADaM), produce statistical analysis plans and scientific reports, build and maintain analytics pipelines, dashboards, and visualizations, ensure data quality and QC, and present results to sponsors while managing multiple projects independently.

Embed in a core business area to define the right problems, design metrics, run analyses and experiments, build reports, surface insights, and drive data-informed decisions end-to-end to improve product, growth, AI/automation, or partnerships.

Embed within a core business area to define problems, build metrics and analyses, run experiments, generate insights from data, and drive decisions end-to-end to improve product, growth, AI/automation, or partnerships.

The Senior Data Scientist will develop machine learning systems for fraud prevention using behavioral data, collaborate across teams, and mentor junior scientists.

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.

Lead end-to-end client ML engagements: scope problems, design, build, and deploy production ML solutions across cloud and containerized stacks. Lead and mentor small teams, manage client relationships, evaluate emerging tools, and ensure models are production-ready and explainable to technical and non-technical stakeholders.

The Senior Data Scientist will develop scalable ML models, analyze large datasets for insights, and collaborate with teams to enhance business performance through data-driven solutions.

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