Senior Machine Learning Engineer (Applied Models)

Senior Machine Learning Engineer (Applied Models) Location: Newark, NJ (Hybrid – 3 days onsite per week) Engagement: Long-term contract with potential to convert Industry: Financial Services We are seeking a Senior Machine Learning Engineer (Applied Models) to join a major financial firm’s Data Science organization. In this role, you will work at the intersection of advanced analytics, applied machine learning, and production systems, partnering closely with engineers, data scientists, and business stakeholders to deliver high-impact analytics and ML solutions. This position is ideal for someone who enjoys hands-on model development, understands the full model development lifecycle, and is comfortable collaborating across engineering and business teams to turn data into scalable, production-ready solutions. What You’ll Do • Lead the hands-on development of advanced data science and machine learning solutions aligned to business objectives and technical requirements • Perform end-to-end model work, including data analysis, model development, training, testing, deployment, and monitoring • Research and evaluate new algorithms, modeling techniques, and analytics approaches to solve complex business problems • Write production-quality code and partner with machine learning engineers to deploy models into production environments • Collaborate with data engineers to build and maintain data pipelines and with software engineers to integrate ML solutions into business platforms • Work closely with business and data science leadership to recommend and develop models supporting customer engagement and wellness use cases • Support experimentation initiatives, including hypothesis development and A/B testing • Manage external vendors involved in data science and machine learning development efforts Required Skills & Experience • Advanced degree (Master’s or Ph.D.) in Mathematics, Statistics, Engineering, Econometrics, Physics, Computer Science, Actuarial Science, Data Science, or a related quantitative field • Experience solving complex analytical problems requiring in-depth evaluation of data, assumptions, and trade-offs • Strong understanding of business concepts and the ability to apply analytics and machine learning to real-world business decisions • Experience designing experiments, conducting research, and working with large-scale customer or behavioral datasets • Excellent problem-solving, communication, and collaboration skills • Ability to continuously learn new techniques, tools, and approaches Technical Experience (Applied Across Several of the Following) Data Acquisition & Transformation • Extracting data from multiple sources using APIs, SQL, and NoSQL • Transforming and preparing data using Python, SQL, and related tools • Data visualization using Python, R, or similar tools Databases & Data Platforms • Strong SQL skills (core proficiency) • Experience working with relational and unstructured data sources • Familiarity with graph or ontology-based databases • Exposure to cloud environments (AWS) and multi-database ecosystems Model Development & Deployment • Understanding of the Model Development Life Cycle (MDLC) • Experience with CI/CD and CT pipelines using tools such as Jenkins, CloudBees, or Harness • Familiarity with ML pipeline frameworks such as MLflow or AWS SageMaker Pipelines • Experience with model and data versioning and production monitoring Statistics & Machine Learning • Strong foundation in calculus, linear algebra, probability, and statistics • Practical application of statistical methods including descriptive, inferential, Bayesian, and time-series techniques • Expertise in machine learning theory and its application to real-world model development • Experience with NLP techniques is a plus Programming Languages • Python (required) • Experience with R, SQL, Java, Scala, or Cypher