Top Machine Learning Jobs in Milwaukee, WI

Build and deploy machine learning models for risk and fraud detection. Lead technical efforts in the risk and fraud domain, use SQL and Python for data preparation and production code, design statistically sound experiments, evaluate model performance, and partner with Operations to respond to evolving financial threats.

Conducts econometric, causal, statistical, and optimization analyses to improve operational efficiency, resource allocation, product performance, and business outcomes. The role defines efficiency measures, develops forecasting and optimization tools, partners with product and engineering teams on data instrumentation and implementation, and communicates findings to executives through clear reports.

Drive enterprise SaaS sales from prospecting through close, expanding adoption among strategic accounts. Engage business and technical decision-makers, including C-suite stakeholders, to address fraud challenges using Sift’s machine learning platform. Build consultative business cases, articulate platform differentiation, collaborate cross-functionally, and help shape sales strategy and growth. Requires strong organizational skills, adaptability, and experience managing complex B2B sales cycles.

Onboard new customers, support product deployment, and create playbooks for customer success and new-hire training. The role works closely with the CEO and CSO at a climate technology startup, addressing ambiguous problems and developing innovative solutions for agriculture and energy customers. Experience in customer success or product management is required; agriculture, SaaS, and early-stage startup experience are preferred.

Designs, builds, and evaluates complete AI systems for Pearson’s K-12 assessment platform. Responsibilities include predictive machine learning, statistical methods, retrieval, knowledge graphs, generative and agentic AI, deterministic services, human review, and system integration. The role develops evaluation datasets and regression suites, analyzes failure modes, supports model adaptation, monitors deployed systems, and collaborates with engineers, psychometricians, learning scientists, and content experts to create transparent, reliable, and auditable AI capabilities.

Develops, integrates, tests, and deploys autonomy, AI, and machine learning algorithms for mission systems. Leads technical and multidisciplinary engineering efforts, delivers production AI systems using LLMs and generative AI, and supports the full software lifecycle from requirements through implementation. Responsibilities include perception, planning, controls, human-machine interfaces, cloud deployment, troubleshooting, mentoring engineers, and guiding technical projects.

Develops data visualization, analytics, machine learning, and automation solutions for coherent optical product manufacturing. Mines wafer-to-module datasets to identify yield drivers, failure signatures, anomalies, correlations, and process drift. Builds and evaluates ML algorithms for yield prediction and root-cause analysis, performs statistical analysis, and automates engineering workflows using GenAI. Collaborates with R&D, manufacturing, and AI/ML teams to optimize product performance and communicate findings to technical audiences.

Researches, designs, develops, trains, and deploys computer vision and machine learning models. Builds and maintains ML software, testing and validation processes, and documentation. Applies image and video processing, deep learning, and computer vision techniques across edge and cloud-based platforms while improving existing applications and performance.

Leads advanced data science and AI initiatives supporting global workplace rankings and research. Develops and maintains statistical models, algorithms, data workflows, benchmarks, and automation for complex survey and organizational data. Translates business methodology into technical requirements, improves production processes, ensures data quality and solution reliability, and manages analytical products through their lifecycle. Collaborates with technical and business stakeholders, modernizes legacy processes, documents controls, and communicates risks, tradeoffs, and technical concepts clearly.

Develop and productionize large-scale machine learning models, ranking algorithms, data pipelines, and real-time or batch systems for Airbnb search and recommendation. Collaborate with engineering, product, operations, and data science teams to prioritize opportunities, drive technical decisions, and measure business impact. Build scalable ML infrastructure supporting feature engineering, model serving, experimentation, interpretability, optimization, and concept drift detection.

Build and maintain a full-stack sewer inspection analysis platform using React, TypeScript, Node.js, PostgreSQL/PostGIS, AWS, computer vision, geospatial analysis, and Temporal.io. Responsibilities include developing interactive dashboards, scalable APIs, video-processing workflows, mapping and risk-assessment tools, microservices, cloud infrastructure, and automated testing and deployment pipelines.

Build and optimize technical infrastructure supporting customer operations, trust and safety, and sales workflows. Develop system integrations, automated workflows, internal support tools, AI-powered customer service solutions, dashboards, and reporting systems. Identify operational bottlenecks, improve processes, and collaborate with product, engineering, operations, customer success, and trust and safety teams to scale efficiently.

Own the vision, roadmap, quality standards, and adoption strategy for catalog APIs powering AI commerce experiences. Lead catalog coverage, product identity, enrichment, ingestion, pricing, availability, freshness, data rights, partner onboarding, metrics, and evaluation initiatives. Partner with engineering, applied science, legal, privacy, security, marketing, and external partners to prioritize investments, guide large senior teams, and scale externally consumed commerce data platforms.

Leads global revenue operations strategy and execution across GTM measurement, account planning, pipeline reporting, territory planning, incentives, pricing, forecasting, operating cadences, and technology. Builds AI-enabled workflows, optimizes the HubSpot, ZoomInfo, and Gong stack, drives adoption, and provides actionable insights to leadership and regional sales and marketing teams.

Applied data scientist responsible for solving data-quality and machine-learning problems, evaluating model performance in production, detecting drift, building ground truth, and prototyping data products. The role uses classification, entity resolution, anomaly detection, embeddings, semantic search, feature engineering, statistical analysis, dashboards, and large-scale production data. The scientist works independently within product or data teams and partners with engineers, analysts, product managers, and subject-matter experts.

Leads the team building LTK’s foundational AI platform, including shared models, services, evaluation frameworks, and production LLM capabilities. Responsibilities include setting the AI roadmap, enabling internal engineering and product teams, overseeing deployment and operational performance, managing quality and cost, and establishing evaluation and monitoring practices. The role also involves hiring, coaching, performance management, and developing a high-performing data science and machine learning organization.

Deploy and customize Palantir Foundry and AIP solutions for enterprise clients. Responsibilities include conducting client workshops, building data pipelines and operational workflows with Python and TypeScript, solving complex technical problems, training end users, documenting solutions, and supporting project delivery. The role requires strong data engineering, communication, analytical, and problem-solving skills, along with recent Palantir experience and approximately 5% travel in Canada and the United States.

Leads the creation of the product organization and enterprise technology strategy for a commercial insurance program administrator. Develops product and AI visions, prioritizes roadmaps, identifies build-versus-buy opportunities, transforms insurance workflows, establishes data and AI governance, and builds high-performing product teams. Partners with executive, underwriting, claims, actuarial, operations, distribution, legal, compliance, engineering, and data leaders to deliver measurable business improvements.

Experienced Data Scientist supporting federal data-driven initiatives through statistical analysis, predictive modeling, machine learning, data preparation, visualization, dashboards, and actionable insights. The role collaborates with technical, business, and program stakeholders, advises on analytical methodologies, improves data quality, and contributes to modernization, automation, and emerging technology efforts.

Own strategic relationships with 8–10 enterprise merchants, driving retention, renewals, expansion, executive engagement, and measurable customer value. Develop account plans, lead executive reviews, negotiate complex renewals and upsells, build ROI cases, maintain Salesforce forecasts, analyze performance data, coordinate cross-functional initiatives, resolve escalations, and communicate customer feedback to Product and leadership. Travel to customer sites and industry events may reach 30%.

Drive enterprise SaaS sales across EMEA by prospecting AI and machine learning stakeholders, conducting discovery, presenting Arize’s platform, managing proof-of-concept engagements, navigating complex sales cycles, closing new business, driving adoption, and expanding customer accounts. The role requires full-cycle enterprise sales expertise, strong communication, independent execution, account research, and relationship-building within the AI/ML community.

Develop and improve AI agents and machine learning models that identify financial opportunities in healthcare contracts, supply chain, pharmacy, and managed care data. Analyze complex datasets, automate manual workflows, build information extraction and agentic capabilities, and establish evaluation frameworks. Collaborate with engineering, product, value engineers, and healthcare subject-matter experts to translate research into scalable solutions with measurable financial impact.

Leads development and deployment of statistical, machine learning, and causal inference models for customer decisioning. Designs experiments, estimates treatment effects and incremental value, evaluates targeting performance, and integrates analytical solutions into governed production systems. Partners with business and technology teams, communicates complex findings, and continuously improves models using experimental and operational results.

Lead actuarial pricing for Lemonade’s car insurance products, including state rating plans, rate indications, predictive modeling, pricing strategy, and rate filing support. Partner with Data Science to integrate machine learning models, monitor pricing and underwriting results, assess competitors, and expand into new states. Manage and develop a team of actuaries while supporting scalable pricing tools and data-driven decision-making.

Leads the global Customer Success organization and owns post-sale strategy, retention, expansion, adoption, customer health, and recurring revenue performance. Builds scalable teams, processes, lifecycle frameworks, and operating systems supporting rapid international growth. Integrates AI, predictive analytics, and automation into Customer Success operations while partnering with Sales, Product, Engineering, Marketing, and Operations. Manages executive relationships with strategic customers, drives forecasting accuracy, and ensures measurable customer outcomes across distributed regions.