Top AI & Machine Learning Jobs

Design and deliver analyses, reports, and dashboards; translate technical findings for business leaders; perform complex data queries and reconciliation; identify data capabilities and partner with enablement teams; mentor analysts and drive a data-driven culture across functions.

Analyzes large datasets using data science, machine learning, predictive modeling, and big data technologies. Designs approaches for data handling, search, retention, storage, and processing; develops software to improve data searches; validates data quality and processes; and reports actionable insights. The role emphasizes scientific analysis over visualization and requires an active Top Secret/SCI clearance with potential Counterintelligence Polygraph eligibility.

Applied Data Scientist focused on incrementality, causal inference, experimentation, Marketing Mix Modeling, and predictive products. The role partners directly with customers to interpret data, diagnose measurement issues, explain statistical results, and provide actionable recommendations. Responsibilities include building diagnostic tools, analyzing model and experiment outcomes, improving methodologies and production capabilities, and collaborating with Product, Engineering, Media Strategy, Customer Success, and senior data scientists.

Lead Osmo’s Applied AI organization building olfactory intelligence models for prediction, formulation, reformulation, molecule generation, and deformulation. Hire and develop ML engineers and research scientists, set the model roadmap and evaluation standards, own enterprise accuracy commitments, design data and annotation strategies, and move research models into reliable production APIs. This hands-on leadership role partners closely with chemistry, sensory science, software, product engineering, and perfumers while remaining involved in code reviews and experimentation.

Develop machine analytics products using Python, SQL, statistical modeling, machine learning, and industrial IoT data. Build web applications, dashboards, APIs, ETL pipelines, and containerized solutions for machine monitoring, anomaly detection, failure diagnosis, forecasting, and technical support. Collaborate with engineering and service teams to translate diagnostic challenges into practical tools and communicate analytical findings to technical and non-technical stakeholders.

Lead and grow Elliptic’s data science team while remaining technically hands-on. Own research direction, datasets, machine learning models, governance, hiring, performance management, and cross-functional delivery. Build capabilities for compliance involving autonomous agents, improve reliability and throughput, and guide model validation, monitoring, and remediation. The role requires strong Python and SQL skills, modern cloud data-stack experience, AI fluency, technical leadership, and expertise analyzing large behavioral or transactional datasets.

The job description provides limited role details. The AI Fullstack Engineer will presumably help develop AI-powered software solutions for higher education institutions, supporting administrative automation, student engagement, and data-driven decision-making. Specific responsibilities, technologies, qualifications, experience requirements, compensation, travel, and clearance information are not provided.

Owns end-to-end delivery of enterprise fine-tuning engagements, including client discovery, use-case prioritization, ROI, scope, timelines, model hosting, SLAs, security reviews, and stakeholder management. Partners with sales, product, engineering, and machine learning teams; navigates enterprise procurement and IT processes; translates between technical buyers and internal teams; and develops repeatable processes and playbooks to scale the practice.

Develop member-level predictive models and longitudinal analyses for CMS Health Outcomes Survey measures and Medicare Advantage Star Ratings. Analyze survey drivers, trajectories, sampling, response rates, case-mix adjustments, and statistical significance. Build data pipelines and dashboards, validate methodologies, deploy models with engineering teams, and translate findings into clinical and operational interventions. Collaborate with Stars, clinical, care management, compliance, legal, and survey vendor stakeholders to ensure CMS-aligned outcomes and reporting.

Drives enterprise AI adoption by understanding business workflows, configuring no-code and low-code agents, apps, and automations, analyzing feedback, developing onboarding materials, and supporting use-case requirements. The role translates business needs into AI solutions, defines KPIs and acceptance criteria, conducts demos, manages support channels, and partners with technical product managers on rollout, adoption, and continuous improvement.