Job responsibilities
- Actively develop thorough understanding of complex business problems and processes; discover opportunities for AI and ML solutions.
- Collaborate with business partners to drive data-led transformations of the businesses.
- Own machine learning development lifecycle activities and execute on crucial timelines and milestones.
- Lead tasks throughout a model development process including data wrangling/analysis, model training, testing, and selection.
- Generate structured and meaningful insights from data analysis and modelling exercise and present them in appropriate format according to the audience.
- Provide mentorship and oversight for junior data scientists to build a collaborative working culture.
- Partner with machine learning engineers to deploy machine learning solutions.
- Own key model maintenance tasks and lead remediation actions as needed.
- Stay informed about the latest trends in the AI/ML/LLM/GenAI research and operate with a continuous-improvement mindset.
Required qualifications, capabilities, and skills
- Advanced degree (MS, PhD) in a quantitative field (e.g., Data Science, Computer Science, Applied Mathematics, Statistics, Econometrics).
- At least 5 years of relevant experience in applied AI/ML domain.
- In-depth expertise and extensive experience with ML projects, both supervised and unsupervised.
- Strong programming skills with Python, R, or other equivalent languages.
- Proficient in working with large datasets and handling complex data issues.
- Experience with broad range of analytical toolkits, such as SQL, Spark, Scikit-Learn, XGBoost, graph analytics, and neural nets.
- Excellent solution ideation, problem solving, communication (verbal and written), and teamwork skills.
Preferred qualifications, capabilities, and skills
- Familiarity with machine learning engineering and developing/implementing machine learning models within AWS or other cloud platforms.
- Familiarity with the financial services industry.