Top Machine Learning Jobs in Leon

Deploy, configure, tune, and support CrowdStrike Falcon AI/ML detection and response capabilities. Optimize ML models, build automation playbooks, integrate AI into customer workflows, develop Falcon API scripts, support incident response and threat hunting, and deliver AI training and customer-facing collateral. Travel up to 25%.

Operate AI-assisted identity migration and demonstration pipelines, review and validate LLM-generated analyses and configurations, and remediate SailPoint Identity Security Cloud environments. Troubleshoot pipeline and product issues, create sales-ready deliverables, support proof-of-value engagements, and improve agentic tools, prompts, playbooks, and evaluation processes. Collaborate with sales, solution engineering, product, professional services, customer success, and forward-deployed engineering teams.

Develop, train, evaluate, and optimize ML solutions and data pipelines; translate business requirements into production-ready ML code; perform feature engineering, model selection, and evaluation; document work, participate in code reviews, and support testing and data issue resolution.

Build end-to-end products from frontend React UIs to backend Python/FastAPI APIs. Develop and productionize Python-based ML/NLP pipelines and model inference APIs. Manage data processing workflows, containerization (Docker/ECS/Fargate), CI/CD, and cloud deployments (AWS or cloud-agnostic). Collaborate in agile teams and focus on scalable architecture and ML productionization.

Drive pricing analytics, margin optimization, competitive intelligence, and AI adoption across Commercial Management. Develop pricing models, predictive analytics, AI-assisted recommendations, dashboards, and executive reporting to improve profitability, forecasting, governance, and commercial decisions. Automate reporting and workflows, monitor competitor and deal intelligence, identify margin leakage and growth opportunities, and present actionable insights to senior leadership.

The Account Executive will develop and close business opportunities, achieve sales quotas, and collaborate with cross-functional teams within the enterprise market.

Monitor and troubleshoot production AI models and agents, tracking performance, latency, errors, availability, drift, and data changes. Manage deployments, versioning, releases, rollbacks, model registries, lineage, and CI/CD pipelines. Integrate monitoring, logging, and alerting platforms while supporting Responsible AI requirements, including fairness, bias, explainability, transparency, and audit trails. Collaborate with product, data science, engineering, and risk teams to standardize AI operations.

As a Data Scientist at Zicasso, you will analyze complex datasets, develop predictive models, and optimize customer experiences using AI and machine learning techniques, while collaborating with cross-functional teams.

Lead strategic enterprise sales for a major financial client: build C-suite relationships, design tailored SaaS analytics solutions, negotiate complex multi-million-dollar deals, advocate client needs internally, drive account expansion and meet quarterly/annual sales targets. Requires collaboration across sales, product, delivery, and marketing and travel up to 50%.

Leads data science strategy across Mexico and Brazil, manages and mentors a distributed team, and remains hands-on building and deploying machine learning models. Establishes modeling standards, infrastructure, delivery practices, and technical roadmaps while partnering with credit leadership on underwriting, valuation, impact measurement, and P&L analysis. Requires strong applied machine learning expertise, prior data science leadership, business communication skills, and understanding of credit or underwriting.

Leads enterprise sales strategy and revenue growth for AI-powered eDiscovery, legal technology, compliance, analytics, and data intelligence solutions. Builds pipelines, manages complex consultative sales cycles, develops executive client relationships, expands strategic accounts, and positions responsible AI adoption. Partners with product, operations, legal, security, marketing, and executive teams on solution development, pricing, contracts, implementation, and market expansion across corporations, law firms, government, and regulated industries.

Lead development of AI-powered topic analytics using machine learning, NLP, and large language models. Design scalable capabilities for topic discovery, classification, clustering, summarization, semantic search, and insight generation across large customer experience datasets. Build production NLP and GenAI pipelines, experimentation frameworks, and hybrid rule-based and ML/LLM solutions while contributing to research and enterprise analytics scalability.

Lead high-complexity data science initiatives improving search ranking, retrieval, relevance, and evaluation. Develop and deploy production machine-learning and NLP solutions, investigate LLM-based ranking features, optimize SQL queries, and work with sparse privacy-preserving data. Define strategies for ambiguous problems, guide other data scientists, collaborate with product teams, and own projects from scoping through production.

Leads enterprise sales strategy and revenue growth for Epiq’s AI-powered eDiscovery, legal technology, compliance, analytics, and information governance solutions. Develops pipelines, manages complex consultative sales cycles, expands strategic accounts, and engages executive stakeholders across corporations, law firms, government, and regulated industries. Acts as an AI and legal technology subject matter expert, supports responsible AI adoption, partners cross-functionally on solution development and delivery, and represents Epiq through market development, thought leadership, and strategic partnerships.

The Machine Learning Researcher will design datasets and evaluate environments for agentic systems, focusing on data quality and benchmarking model performance in RL settings. They will collaborate with teams to translate workflows into evaluative frameworks and improve the understanding of high-quality datasets.

Lead development and optimization of composites molding processes, execute engineering test plans, apply SPC and data analytics for root-cause and capability improvements, collaborate across Technology, Quality, Production, and Customers, mentor engineers, and implement manufacturing readiness and automation technologies. Requires on-floor presence and up to 25% travel between US and Mexico.

Lead machine learning initiatives, oversee product development, improve execution pace, enhance team reliability, and foster team growth at Hightouch.

The Director of Research will lead DataLab at Protege, defining the research agenda and managing a high-performing team focused on AI data challenges and methodologies.

Lead Salesforce product initiatives for Channel and Enterprise sales: gather and document requirements, build roadmaps, define solutions, drive implementation, enable adoption, produce reports, and partner with sales, sales ops, IT and enablement to improve selling effectiveness and reduce CRM admin burden.

Lead statistical activities for clinical trials from protocol design through reporting. Provide statistical oversight, collaborate with cross-functional teams and CROs, apply SAS/R (and ML) for analyses, automate processes, and mentor junior statisticians.

This role involves leading strategic planning, product innovation, and cross-functional collaboration to enhance pharmacy analytics and drive growth initiatives for CVS Health.

Create and review realistic, locale-specific robotics and natural sciences tasks in Spanish (LATAM), apply scoring rubrics, evaluate AI and human responses for accuracy and regulatory alignment, and produce gold-standard solutions and expert feedback.

Design, build, and maintain production-grade ML systems for large-scale legal NLP datasets. Develop and orchestrate ML/AI agents, prototype solutions, collaborate with cross-functional teams, and work with legal data annotators to improve data quality and drive product-focused outcomes.

Operate and monitor AI models and agent systems in production. Track performance, drift, latency, errors, availability, and business impact; manage incidents, rollbacks, deployments, versioning, registries, and lifecycle transitions. Maintain governance, audit trails, risk controls, fairness, bias, explainability, and transparency monitoring. Integrate cloud, container, monitoring, logging, alerting, and CI/CD platforms while collaborating with product, engineering, data science, and risk teams.

Develop and validate change-detection and anomaly models on multi-temporal Sentinel imagery for pipeline corridors. Fine-tune geospatial foundation models with LoRA/PEFT, implement SAR displacement methods and dune-migration tracking, engineer data ingestion from Copernicus/STAC sources, design labelling and validation strategies, and communicate results to technical and business stakeholders.