iScale Solutions

Role Description - Lead implementation and delivery on key product and technology initiatives - Be hands-on in the codebase while mentoring and unblocking other engineers - Work closely with engineering leadership to shape and prioritise the roadmap - Contribute to architectural decisions and system design - Ensure quality, security, and performance standards are upheld - Support sprint planning, backlog grooming, and team rituals - Champion engineering best practices across the team - Collaborate with enterprise customers and partners when needed Qualifications - 4+ years in senior engineering roles - A strong background in software development (hands-on) - Proficiency with AWS and modern cloud infrastructure, with hands-on experience working with serverless architecture - Solid experience with TypeScript, both front-end (with React) and back-end with Node.js - Experience mentoring engineers and building a culture of collaboration across a high-performing team - Strong understanding of software architecture and system design - Experience in start-up/scale-up environments Requirements - Experience in payments or financial services (nice to have) - Working knowledge of PostgreSQL (nice to have) - Familiarity with Infrastructure as Code (Terraform, AWS CDK, etc.) (nice to have) - Experience with APIs, payment gateways, and third-party integrations (nice to have) - Exposure to PCI, ISO27001 and SOC2 certifications and compliance (nice to have) - Comfortable working with large enterprise partners or customers (nice to have)

Role Description - Optimize ML model serving for low-latency inference (target: sub-200ms P95) on EKS - Advise on and implement AWS-native ML infrastructure (SageMaker endpoints, model registry, A/B testing, monitoring) - Support ML-optimized rule weight calibration — training logistic regression / LightGBM on rule-fire indicators to learn optimal rule weights from labeled data - Assist with model retraining pipeline automation and drift detection - Contribute to model explainability documentation (SHAP-based attribution) for regulatory compliance - Participate in model governance: version control, audit trails, threshold configuration per participating institution - Support load testing and performance benchmarking of the ML scoring pipeline - Provide input for the technical proposal and architecture documentation Qualifications - AWS Machine Learning Specialty Certification (or AWS Certified Machine Learning Engineer – Associate) — current and valid - 3+ years of hands-on experience deploying ML models in production on AWS - Strong Python skills (scikit-learn, LightGBM/XGBoost, pandas) - Experience with containerized ML serving (Docker, Kubernetes/EKS) - Familiarity with model monitoring, drift detection, and retraining pipelines Requirements - Experience in fraud detection, AML, or financial risk systems - Familiarity with graph-based ML (GNN, NetworkX) for network analysis - Experience with Apache Kafka or Apache Flink for streaming ML - Knowledge of SHAP or other model explainability frameworks - Experience with SageMaker (endpoints, model registry, pipelines) Benefits - Fully Remote - Flexible working hours (part-time, ~15–20 hours/week) - Potential to extend engagement based on project phase progression

This is a remote position. - Make messy data decision-ready. Profile, cleanse, reconcile, and document datasets so insights are reliable—even when the inputs are noisy. - Support new, “tip-of-the-spear” apps. Work with the application team to define the right data ingredients, stand up thin-slice pipelines, and validate results. - Move fast—carefully. Turn vague asks into testable hypotheses, build the analysis or dashboard, and ship quick iterations with attention to detail. - Guard data quality. Set up checks, audits, and alerts; track issues to closure and raise the bar on definitions, lineage, and reproducibility. - Automate the repetitive. Use SQL/Python (plus Airflow/Prefect/dbt or similar) to turn one-off analyses into lightweight, maintainable workflows. - Tell the story. Present findings with clear business context; highlight signal vs. noise, trade-offs, and recommended next steps. - Partner across functions. Collaborate with product, engineering, and client teams to ensure what we build solves real problems. - Utilize AI to improve business functions. Apart from automation, work with AI during project delivery and data mining Requirements - Minimum 5+ years of experience in Data Analytics, Data Engineering, or similar roles (Mid–Senior level) - Advanced SQL (Non-Negotiable) – must be comfortable writing complex queries - Strong Python experience for data analysis, automation, or data pipelines - Experience working with Big Data technologies (Hadoop ecosystem) - Experience building or supporting data pipelines - Familiarity with Airflow or similar workflow orchestration tools - Experience working with large or messy datasets (data cleansing, validation, etc.) - Ability to automate data processes instead of doing one-off analysis - Strong communication skills and ability to explain insights clearly - Comfortable working with cross-functional teams Nice to Have - Exposure to AI or AI-related data projects - Experience with modern data platforms (Snowflake, BigQuery, Databricks, Athena, etc.) Benefits - Competitive Salary Package: Receive a pay package that matches your skills and experience. - Vacation and Sick Leave credits: Enjoy vacation and sick leave credits to maintain work-life balance. - Health Coverage: Get medical, dental, and vision insurance for you and your dependents. - Government-Mandated Benefits: Full coverage of all statutory benefits like SSS, PhilHealth, and Pag-IBIG. - Learning Opportunities: Access training, certifications, and mentorship to grow your career. - Team Engagement: Join team-building activities and wellness programs. - Modern Tools: Use the latest technology to excel in your role. - Career Growth: Clear paths for promotion and professional development. - Inclusive Culture: Be part of a diverse, supportive, and collaborative global team. Referral Rewards: Earn bonuses for bringing great talent to the team.

This is a remote position. - Turn messy data into insights — explore, profile, and reconcile datasets so analyses are reliable. - Solve ambiguous problems with data — build segments, proxies, or intersections across datasets when clean definitions don’t exist. - Work closely with product and engineering to define data ingredients for new applications and experiments. - Deliver fast analytical iterations using Snowflake, SQL, Python, and Jupyter. - Use AI tools to accelerate analysis, coding, and debugging. - Operationalize repeatable work using Python, Airflow, and Git. - Maintain data quality through validation checks, documentation, and careful analysis. - Communicate insights clearly, focusing on signal, trade-offs, and business impact. Requirements - Strong SQL & Python experience - Comfortable with exploratory analysis - Ability to find signal in messy or incomplete datasets - Experience turning analyses into repeatable workflows (Airflow, Git) - Strong instincts for data quality and validation - Curiosity, clear communication, and a practical problem-solving mindset - Comfortable taking ownership of problems and working independently to move analysis forward - Staying informed on industry and technology trends to better contextualize data insights Benefits - Competitive Salary Package: Receive a pay package that matches your skills and experience. - Vacation and Sick Leave credits: Enjoy vacation and sick leave credits to maintain work-life balance. - Health Coverage: Get medical, dental, and vision insurance for you and your dependents. - Government-Mandated Benefits: Full coverage of all statutory benefits like SSS, PhilHealth, and Pag-IBIG. - Learning Opportunities: Access training, certifications, and mentorship to grow your career. - Team Engagement: Join team-building activities and wellness programs. - Modern Tools: Use the latest technology to excel in your role. - Career Growth: Clear paths for promotion and professional development. - Inclusive Culture: Be part of a diverse, supportive, and collaborative global team.