Senior Analytics Engineer

As a Senior Analytics Engineer I within the ISS Data Engineering team at Fidelity, you will play a crucial role in transforming the organization's analytics and AI capabilities. Your primary responsibilities will include leading the design and implementation of machine learning solutions for complex investment challenges, establishing model development standards, designing robust ML pipelines, implementing model governance practices, creating API and containerization solutions, building monitoring systems, and mentoring junior team members in machine learning techniques. Key Responsibilities: - Lead design and implementation of machine learning solutions for complex investment challenges - Establish model development standards and implement explainable AI aligned with business needs - Design robust ML pipelines using Python scientific libraries and composite AI techniques - Implement model governance, versioning, and lifecycle management practices - Create API and containerization solutions for seamless model deployment - Build monitoring systems for ongoing quality control of AI implementations - Mentor junior team members in machine learning techniques and best practices - Partner with stakeholders to translate business requirements into actionable analytics solutions Qualifications Required: - 10-15 years of total professional experience with 5+ years in machine learning and analytics - Advanced SQL skills across different database platforms (particularly Snowflake) - Extensive experience building and deploying supervised learning models, including regression techniques and classification - Expertise in model explainability, feature engineering, and optimization techniques - Proven ability to apply composite AI techniques to solve complex business problems - Strong understanding of model management, versioning, and governance practices - Proficiency with Python ML libraries (scikit-learn, numpy, matplotlib) and data exploration tools - Experience in API management and containerization for ML deployment - Excellent problem-solving skills and cross-functional communication abilities Desired Skills & Experience: - Knowledge of financial services domain, particularly investment management - Experience with neural networks, deep learning, and other advanced AI techniques - Familiarity with Apache Superset and data exploration tools like Open Refine - Experience with LLM integration and RAG implementations - Experience with MLflow for model lifecycle management - Familiarity with Go and R programming languages - Experience with cloud platforms, preferably AWS Education: - Masters or bachelors degree in computer science, Data Science, Statistics, or related field - Advanced certifications in machine learning, AI, or data science preferred At Fidelity, you will be part of a team that values your wellbeing, supports your development, and offers a comprehensive benefits package. The company is committed to providing a flexible work environment that promotes work-life balance and motivates you to be part of the team. To explore more about Fidelity International and potential career opportunities, visit careers.fidelityinternational.com. As a Senior Analytics Engineer I within the ISS Data Engineering team at Fidelity, you will play a crucial role in transforming the organization's analytics and AI capabilities. Your primary responsibilities will include leading the design and implementation of machine learning solutions for complex investment challenges, establishing model development standards, designing robust ML pipelines, implementing model governance practices, creating API and containerization solutions, building monitoring systems, and mentoring junior team members in machine learning techniques. Key Responsibilities: - Lead design and implementation of machine learning solutions for complex investment challenges - Establish model development standards and implement explainable AI aligned with business needs - Design robust ML pipelines using Python scientific libraries and composite AI techniques - Implement model governance, versioning, and lifecycle management practices - Create API and containerization solutions for seamless model deployment - Build monitoring systems for ongoing quality control of AI implementations - Mentor junior team members in machine learning techniques and best practices - Partner with stakeholders to translate business requirements into actionable analytics solutions Qualifications Required: - 10-15 years of total professional experience with 5+ years in machine learning and analytics - Advanced SQL skills across different database platforms (particularly Snowflake) - Extensive experience building and deploying supervised learning models, including regression techniques and classification - Expertise in model explainability, feature engineering, and optimization techniques - Proven ability to apply composite AI techniques to solve complex business problems - Strong under