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JPMorgan Applied AIML Data Scientist Lead - Vice President 
United States, New Jersey, Jersey City 
580806588

14.09.2024

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.