Machine Learning Engineer - Python and SQL

About the Role

The Specialized Analytics Analyst is an emerging professional role within the TTS Analytics organization. The role requires applying data science, machine learning, and Generative AI techniques to solve complex business problems, generate actionable insights, and deliver scalable analytical solutions. The individual will work closely with business, product, operations, and technology partners to translate data into measurable business outcomes.

About TTS Analytics

The Treasury & Trade Servces (TTS) Analytics team provides advanced analytical solutions and strategic insights to Product, Pricing, Client Experience, and Sales teams across the global TTS business.

Our mission is to leverage data, advanced analytics, machine learning, and Generative AI to:

• Improve client experience and engagement
• Drive acquisition, cross-sell, retention, and revenue growth
• Identify business opportunities through data-driven insights
• Build and scale AI-powered solutions that generate measurable business impact

Key Responsibilities

The successful candidate will:

Advanced Analytics & Data Science

• Analyze large and complex datasets to identify patterns, trends, risks, and business opportunities.
• Develop analytical frameworks to solve business problems related to client experience, pricing optimization, customer acquisition, cross-sell, and retention.
• Translate ambiguous business requirements into structured analytical and data science solutions.

Machine Learning Development

• Design, develop, validate, and deploy machine learning models
• Deliver models that generate measurable business value and support decision-making.

• Develop and enhance Generative AI applications leveraging:

• Design, build, and maintain scalable ETL/ELT pipelines and data workflows supporting analytics and AI initiatives.
• Partner with data engineering teams to improve data quality, lineage, governance, and platform scalability.

Stakeholder Management

• Collaborate with business, product, operations, technology, and data teams to prioritize initiatives and deliver impactful solutions.
• Present analytical findings, model outcomes, and recommendations effectively to both technical and non-technical stakeholders, including senior management.

Required Qualifications

Education

• Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Engineering, Mathematics, or a related quantitative discipline.

Experience

• 3 to 5 years of experience in Data Science, Machine Learning, Advanced Analytics, or related disciplines.
• Experience working within banking, financial services, or other highly regulated industries is preferred.
• Demonstrated experience deploying production-grade machine learning or AI solutions.

Technical Skills

Programming & Data Management

• Strong proficiency in Python (required)
• Advanced SQL skills
• Experience with PySpark and large-scale data processing

Machine Learning & Statistics

Hands-on experience with:

• Supervised and unsupervised learning techniques
• Statistical modeling and hypothesis testing
• Experiment design and A/B testing
• Scikit-learn
• XGBoost and/or LightGBM

• ETL/ELT pipeline development
• Data modeling
• CI/CD implementation for ML workloads
• Model monitoring and performance management
• Version control and deployment frameworks

Core Competencies

Analytical Excellence

• Strong analytical and problem-solving skills.
• Ability to identify trends, patterns, and actionable insights from structured and unstructured data.
• Ability to formulate analytical methodologies and independently execute complex analyses.

Communication & Collaboration

• Strong verbal and written communication skills.
• Ability to influence stakeholders through data-driven recommendations.
• Comfortable working in cross-functional and matrixed organizations.

Execution & Ownership

• Detail-oriented with strong organizational skills.
• Ability to manage multiple priorities and deliver in a fast-paced environment.
• Strong sense of accountability and ownership for outcomes.

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