Financial Services Focus
We are seeking a highly experienced Machine Learning Engineer to lead our data classification initiatives. This role requires a unique blend of deep machine learning expertise proficiency in Python and specialized knowledge of data handling within the heavily regulated financial services industry. You will develop and deploy intelligent systems to automatically identify categorize and manage sensitive data across relational and non-relational databases.
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ML Solution Design & Deployment: Design build train and deploy machine learning models specifically for automated data classification across diverse data stores (SQL NoSQL data lakes).
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Financial Services Data Expertise: Apply a deep understanding of financial data types (e.g. PII account numbers transaction data) to tune models for accuracy and relevance within the industry context.
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Regulatory Compliance Implementation: Ensure ML models and classification rules adhere strictly to stringent regulatory standards across multiple industries (e.g. GDPR CCPA PCI-DSS SOX Basel Accords).
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Containerized ML Operations: Utilize modern MLOps practices deploying and managing machine learning models within containerized environments (Docker) for scalability and reproducibility.
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Technology Stack Proficiency: Primarily develop using Python ML frameworks while leveraging expertise in Java and JavaScript for integration with existing enterprise systems and front-end classification interfaces.
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Tooling & Frameworks: Utilize expertise in various machine learning frameworks and data science platforms.
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Methodology & Rules: Implement robust data classification methods leveraging both statistical ML approaches and expert-defined business rules for high-precision results
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The ideal candidate would have a strong technical aptitude and a flair for picking up new skills
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The role provides ample opportunity for learning and career growth and is only constrained by the individual’s appetite for learning
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8+ years of IT experience
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ML Expertise: Proven experience as a Machine Learning Engineer with a strong portfolio of deployed ML solutions particularly those involving natural language processing or data categorization.
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Python Mastery: Expert-level proficiency in Python and relevant ML libraries (e.g. TensorFlow PyTorch Scikit-learn Pandas) for building sophisticated classification models.
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Database Fluency: Hands-on experience working with data housed in both relational databases (e.g. PostgreSQL Oracle DB2) and non-relational databases (e.g. MongoDB Cassandra).
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Financial Services Data: In-depth knowledge of data structures governance needs and sensitive information types prevalent in the financial services sector.
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Regulatory Knowledge: Strong understanding of global data privacy and compliance regulations applicable to data management.
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Containerization: Practical experience with Docker and/or Kubernetes for deploying scalable microservices and ML models.
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Diverse Tooling Experience: Familiarity with a range of data science and analytics platforms such as MATLAB Altair RapidMiner or IBM SPSS Statistics.
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Java & JavaScript Experience: Proficiency in Java for backend integration and/or JavaScript for front-end interface development related to data governance platforms is highly desirable.
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Data Governance Tools: Familiarity with broader data governance and catalog tools (e.g. Informatica Alation) and how ML integrates with these platforms.
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Certifications: Relevant certifications in Machine Learning or Data Governance are a plus.