Top Machine Learning Jobs in Caloocan

Analyze PB-scale trading, transactional, operational and customer data; build visualizations, ML models and experiments; translate findings into actionable recommendations for trading, operations, product and executives in a fast-paced environment.

Work with Product, Business, and Engineering to collect, clean, analyze data, build Python and SQL automation, contribute to internal analytics platforms, assist cloud deployments, and document technical solutions to support decision-making.

Responsible for driving adoption of the FICO Platform by educating clients on its capabilities, managing sales targets, and building strategic relationships with senior executives, particularly in financial institutions.

The role involves leading strategic client accounts, managing complex deal structures, and collaborating with internal teams to create tailored solutions. Responsibilities include business growth, client advocacy, and market leadership while maintaining relationships with senior stakeholders in financial institutions.

Lead design and delivery of analytics, automation, and AI-enabled data solutions to improve operational efficiency across Asia Pacific. Build data pipelines, models, dashboards, desktop apps and Excel add-ins; advise stakeholders, manage projects, establish standards, and coach junior team members.

The Sales Manager will drive revenue growth and expand market share in the Philippines through strategic sales initiatives, client relationship building, and leading sales cycles.

Develop and maintain Power Platform-based BI solutions and automations, perform ETL and data cleansing, build Power BI dashboards and predictive risk models, integrate AI/ML into reporting, and support cybersecurity, IT, and finance data collection and visualization for senior leadership and operational teams.

Design, implement and support web and mobile (Android/iOS/hybrid) software solutions as part of a Scrum team. Attend client meetings, communicate progress, learn deployment processes, and travel for on-site assignments and training.

Lead NAV operations and fund administration activities, ensuring accurate NAV calculation, regulatory compliance, client service, team leadership, and technology-enabled process improvement.

The AI Solutions Architect will translate business objectives into AI strategies, design solution architectures, oversee integration into enterprise systems, ensure compliance, and maintain technical governance while staying updated on AI trends.

Design, build, and deploy scalable ML models and pipelines from research to production. Own feature engineering, model tuning, real-time prediction, MLOps automation, monitoring, and lifecycle management to solve visibility, forecasting, and freight audit problems.

Lead end-to-end ML lifecycle: research SOTA architectures, fine-tune foundation models, design data pipelines, deploy models with MLOps (Docker/Kubernetes/CI-CD), build APIs, implement model governance, and collaborate within cross-functional agile squads.

Own and grow BioCatch accounts in the Philippines (primarily banking/financial services). Build strategic account plans, prospect and close new business, upsell and cross-sell, collaborate with APAC commercial teams, manage pipeline/forecasting, represent BioCatch at events, and drive customer satisfaction and technology adoption.

Own end-to-end ML model lifecycle from EDA and feature engineering to production deployment and monitoring. Collaborate with cross-functional teams to identify modeling opportunities, evaluate performance and business impact, integrate new data sources, and ensure scalable, observable production systems.

Design, implement, and optimize production-ready machine learning models and pipelines. Collaborate with data, software, and product teams to integrate ML solutions. Perform data preprocessing, feature engineering, EDA, training, validation, deployment, and monitoring. Maintain environment setup, scalable APIs, and database architecture. Document work and stay current with ML research and best practices.

Design, automate, and manage end-to-end ML pipelines and scalable infrastructure on Azure. Build CI/CD with GitHub Actions, containerize models with Docker and AKS, monitor model lifecycle and data drift, implement cost controls, and collaborate with data science, DevOps, and IT to deploy resilient, production-grade ML solutions.