Compliance - Applied AI/ML Lead - Vice President

We need to output HTML fragment with sections: About the role? The description includes "Job Description" intro, then responsibilities, required qualifications, preferred qualifications, About Us, About The Team. We need to use

for section headings like About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. But we should only include sections that have real specific content. The posting includes responsibilities, required qualifications, preferred qualifications, about us, about the team. It does not mention Pay, Schedule, Benefits specifics (just generic statement about benefits but no specifics). The instruction: never create Pay/Schedule/Benefits sections unless posting actually states them. It mentions "We offer a competitive total rewards package including base salary determined based on the role, experience, skill set and location. Those in eligible roles may receive commission-based pay and/or discretionary incentive compensation... We also offer a range of benefits and programs..." This is generic but still mentions benefits. However it's vague; but it's still stating benefits exist. The instruction says: never emit a heading followed by filler/placeholder text (e.g. "commensurate with experience", "not specified", "N/A", "varies", "TBD") and never create Pay/Schedule/Benefits sections unless the posting actually states them. It does state benefits generally, but not specific benefits. Might be considered filler? It says "We also offer a range of benefits and programs to meet employee needs, based on eligibility. These benefits include comprehensive health care coverage, on-site health and wellness centers, a retirement savings plan, backup childcare, tuition reimbursement, mental health support, financial coaching and more." That's specific benefits list. So we can include a Benefits section with that list. Pay: It mentions base salary determined based on role, experience, skill set and location; commission-based pay and/or discretionary incentive compensation. That's somewhat specific but still generic. Could we include Pay section? It does mention pay components. Might be okay. However instruction: prefer a few substantive sections over many thin ones. Might combine Pay and Benefits into Compensation? But headings prescribed: Benefits, Pay, Schedule. We could include Benefits and Pay if they have content. Schedule not mentioned, so omit. We also need to not repeat job title as heading. No Job ID. We need to use

for section headings. Use

• for any list of duties/requirements/benefits. Use

  for prose. We need to preserve all substantive information; do not invent or summarize away detail. We need to remove boilerplate: cookie/navigation/legal/EEO/equal-opportunity/scam-warning boilerplate, duplicated lines, and application instructions. The posting includes EEO statements etc. We should remove those. Also remove "About Us" maybe we can keep as section? The headings list includes About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. "About Us" not in list; but we could treat as "About the role"? Actually "About the role" is a heading we can use for introductory description. The posting has "Job Description Bring your expertise..." That's like about the role. We'll use

  About the role

  with prose. Responsibilities: we have list. Requirements: maybe combine Required Qualifications, Capabilities, And Skills as Requirements. Use

  Requirements

  with list. Qualifications: maybe Preferred Qualifications as Qualifications? But we already used Requirements for required. Could have Qualifications for preferred. Or Skills for specific skills list. The posting includes many specific skills under Required Qualifications. Could split: Requirements for experience/education, Skills for technical skills list. But we need to follow instruction: use headings only if real specific content. We have both required and preferred qualifications. Could make Requirements for required qualifications, Qualifications for preferred qualifications, Skills for the list of packages etc. However the required qualifications already includes skills list. Might be redundant. Better: Use Requirements for required qualifications (including experience, education, skills). Use Qualifications for preferred qualifications. Use Skills maybe not needed. But we need to avoid thin sections. Let's decide: We'll have About the role, Responsibilities, Requirements (required qualifications), Qualifications (preferred qualifications), Benefits, Pay. We need to ensure we don't create sections with filler. All have content. Now extract content. About the role: The introductory paragraph: "Bring your expertise to JPMorgan Chase. As part of Risk Management and Compliance, you are at the center of keeping JPMorgan Chase strong and resilient. You help the firm grow its business in a responsible way by anticipating new and emerging risks, and using your expert judgement to solve real-world challenges that impact our company, customers and communities. Our culture in Risk Management and Compliance is all about thinking outside the box, challenging the status quo and striving to be best-in-class. As a Data Scientist Vice President within the Compliance, Conduct Operational Risk Data Analytics organization, you will be responsible for devising and developing Proofs of Concept (POCs) and deployable models using AI/ML techniques, algorithms and other statistical and numerical methods. You will need to able to extract and work with large volumes of data (both structured and unstructured) from multiple sources, transforming it into an analysis-ready format to develop the data pipeline. Additionally, you are expected to independently formulate methodologies, and quantitative and analytical tasks, from business problems." We'll put that in

  . Responsibilities: list bullet points: - Analyze complex/unstructured data to understand the business problem and use case - Analyze business requirements, design, and develop appropriate methodology - Develop deployable, scalable and effective models/ analytical methods as part of technology managed system or as a self-served application of a business user - Work collaboratively and creatively with other data scientists, technology partners, risk professionals, model validation teams, etc. - Prepare technical documentation of quantitative models for internal model risk and governance review We'll put in

  • . Requirements (required qualifications): The block: "Required Qualifications, Capabilities, And Skills 6+ years of related experience in Python, R or Scala with Bachelor of Science degree in Computer Science, Physical Sciences, Econometrics, Statistics, or other any quantitative discipline.Demonstrable theoretical and application knowledge of Machine Learning methods, and/or Statistical ModelsDemonstrable hands-on experience and familiarity with any or all of the following packages, algorithms, and/or alternatives, including Graph Learning Packages : (NetworkX, Torch-Geometric, Graphframes, Graphistry),ML Packages (Pandas, Scikit-Learn, XGBoost, catboost, lightgbm, automl, Optuna, Hyperopt), Visualization Packages (Matplotlib, Seaborn, Geopandas), Algorithm (Ensemble Louvian / Hierarchical Clustering, Label Propagation, Connected Component Analysis, Graph Neural net (Graph Attention Network), Page Rank, Centrality Analysis, Tree based Analysis, Outlier Detection Methods, Zero Shot/ Few Shot learning)Demonstrable experience with LLM prompt engineering and open source LLM fine tuning for a specific domainDemonstrable experience with agentic solution development and insights on how to improve consistency and reliability in agentic behaviorHands-on professional experience in software development especially with analytical & computationally intensive systems, digital transformations leveraging cloud technologies (AWS, GCP, Azure, Databricks etc.)Experience in developing and operationalization of data pipelines Familiarity and experience of assimilating large amounts of data from multiple databases and utilize them for creating actionable outcome; Adhering to a standardized analysis and project methodology; and Documenting quantitative analysis" We need to parse into sensible list items. Could break into: - 6+ years of related experience in Python, R or Scala - Bachelor of Science degree in Computer Science, Physical Sciences, Econometrics, Statistics, or other quantitative discipline - Demonstrable theoretical and application knowledge of Machine Learning methods and/or Statistical Models - Demonstrable hands-on experience with Graph Learning Packages (NetworkX, Torch-Geometric, Graphframes, Graphistry) - Demonstrable hands-on experience with ML Packages (Pandas, Scikit-Learn, XGBoost, catboost, lightgbm, automl, Optuna, Hyperopt) - Demonstrable hands-on experience with Visualization Packages (Matplotlib, Seaborn, Geopandas) - Experience with algorithms: Ensemble Louvian/Hierarchical Clustering, Label Propagation, Connected Component Analysis, Graph Neural net (Graph Attention Network), Page Rank, Centrality Analysis, Tree based Analysis, Outlier Detection Methods, Zero Shot/Few Shot learning - Demonstrable experience with LLM prompt engineering and open source LLM fine tuning for a specific domain - Demonstrable experience with agentic solution development and insights on improving consistency and reliability in agentic behavior - Hands-on professional experience in software development especially with analytical & computationally intensive systems, digital transformations leveraging cloud technologies (AWS, GCP, Azure, Databricks etc.) - Experience in developing and operationalization of data pipelines - Familiarity and experience of assimilating large amounts of data from multiple databases and utilize them for creating actionable outcome; adhering to a standardized analysis and project methodology; and documenting quantitative analysis We'll put each as
  • . Qualifications (preferred): block: "Post graduate degrees such as Master's Degree, PhD, etc. is preferredWorking knowledge of C/C#/C++ or others is a plusReal life exposure to Agile SDLC, ModelOps and /Or Design Thinking is desirable.Familiarity with Natural Language Processing techniques is a plusSelf-starter and strong influencing skills with strong communication skillsExperience in financial services industry and/ or, experience with process, controls and governance of a highly regulated environment" We'll break into list items: - Post graduate degrees such as Master's Degree, PhD, etc. preferred - Working knowledge of C/C#/C++ or others is a plus - Real life exposure to Agile SDLC, ModelOps and/or Design Thinking desirable - Familiarity with Natural Language Processing techniques is a plus - Self-starter and strong influencing skills with strong communication skills - Experience in financial services industry and/or experience with process, controls and governance of a highly regulated environment Benefits: list from posting: "We also offer a range of benefits and programs to meet employee needs, based on eligibility. These benefits include comprehensive health care coverage, on-site health and wellness centers, a retirement savings plan, backup childcare, tuition reimbursement, mental health support, financial coaching and more." We'll make list items for each benefit. Pay: content: "We offer a competitive total rewards package including base salary determined based on the role, experience, skill set and location. Those in eligible roles may receive commission-based pay and/or discretionary incentive compensation, paid in the form of cash and/or forfeitable equity, awarded in recognition of individual achievements and contributions." We'll make list items maybe: base salary determined by role, experience, skill set, location; commission-based pay and/or discretionary incentive compensation (cash and/or forfeitable equity) for eligible roles. We'll put as
    • . We must not include boilerplate like EEO statements, "About Us" history, "About The Team". We should remove those. The instruction: remove cookie/navigation/legal/EEO/equal-opportunity/scam-warning boilerplate, duplicated lines, and application instructions. The "About US" and "About The Team" are not boilerplate but maybe considered company description; but they are not in prescribed headings list. Could we keep them under About the role? Might be considered extra but not required. The instruction says preserve ALL substantive information; do NOT invent or summarize away detail. So we should keep that info somewhere. But we have limited headings; we could incorporate that info into About the role section as prose. That would preserve it. So we can add after the initial description a paragraph about JPMorganChase history and corporate functions. That would be substantive. Thus About the role will include both the role description and the company overview and team description. Let's craft About the role paragraph(s). Could be multiple

      tags. We'll output:

      About the role