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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Job Family Group: Decision Management
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Job Family:Specialized Analytics (Data Science/Computational Statistics)
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Time Type:Full time
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Most Relevant Skills Please see the requirements listed above.
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Other Relevant Skills For complementary skills, please see above and/or contact the recruiter.
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