Role overview
The AI/ML Engineer will analyze complex data sets to uncover meaningful patterns and translate them into actionable insights using predictive modeling, data mining, and machine learning techniques. This role will collaborate closely with senior team members to design and implement scalable AI solutions across enterprise functions.
The ideal candidate is technically strong, analytically rigorous, and motivated to apply emerging AI technologies to real-world business challenges in a fast-paced, collaborative environment.
Responsibilities
- Partner with senior AI/ML engineers to design, build, and deploy machine learning and deep learning solutions
- Develop models such as classification, forecasting, propensity modeling, uplift modeling, and foundational model fine-tuning
- Identify opportunities to enhance underwriting, claims operations, risk evaluation, customer experience, and other business processes through AI-driven insights
- Collaborate cross-functionally with business, technology, and transformation teams to ensure scalable and production-ready implementation
- Translate complex analytical findings into clear, actionable recommendations for non-technical stakeholders
- Contribute to continuous improvement of modeling methodologies and deployment practices
Basic qualifications
- Master’s degree in Statistics, Data Science, Mathematics, Computer Science, Operations Research, or a related quantitative field (Ph.D. preferred)
- 2+ years of experience applying advanced analytics and machine learning techniques (e.g., logistic regression, decision trees, neural networks, random forests, etc.)
- 2+ years of strong hands-on experience in Python or R
- Experience working with both structured and unstructured data, including digital and CRM datasets
- Demonstrated ability to cleanse, integrate, and model large, complex datasets
- Strong communication skills with the ability to explain technical findings in business terms
Preferred qualifications
- Internship or project experience in AI engineering, machine learning, or related quantitative disciplines
- Experience within financial services, risk-based, or regulated industries
- Familiarity with actuarial methodologies or domain-specific risk datasets
- Self-motivated learner with a passion for staying current on advancements in AI and machine learning
- Ability to thrive in a collaborative, team-oriented, fast-paced environment
Tags & focus areas
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