Manager, Data Science - US Card Customer Management

We need to produce HTML fragment with sections: About the role? The posting includes description: "Data is at the center..." etc. We need to extract sections: maybe "About the role", "Responsibilities", "Requirements", "Qualifications", "Skills", "Benefits", "Pay", "Schedule". We must only include sections that have real specific content. We must not repeat job title. No Job ID. Remove boilerplate. We need to parse the posting. Let's extract content: First paragraph: "Data is at the center of everything we do. As a startup, we disrupted the credit card industry by individually personalizing every credit card offer using statistical modeling and the relational database, cutting edge technology in 1988! Fast-forward a few years, and this little innovation and our passion for data has skyrocketed us to a Fortune 200 company and a leader in the world of data-driven decision-making. As a Data Scientist at Capital One, you’ll be part of a team that’s leading the next wave of disruption at a whole new scale, using the latest in computing and machine learning technologies and operating across billions of customer records to unlock the big opportunities that help everyday people save money, time and agony in their financial lives." Then "Team Description As part of the US Card data science organization, this team focuses on the customer management business, and builds top tier machine learning solutions to meet the various needs such as real-time customer behavioral prediction and valuation forecasts. We work in a team environment, collaborating closely with cross-functioning teams including the business, engineers, analysts, data science/quants, products." Then "Role Description In this role, you will:" then bullet list (actually sentences separated by line breaks?): - Partner with a cross-functional team of data scientists, software engineers, and product managers to deliver a product customers love - Leverage a broad stack of technologies — Python, Conda, AWS, H2O, Spark, and more — to reveal the insights hidden within huge volumes of numeric and textual data - Build machine learning models through all phases of development, from design through training, evaluation, validation, and implementation - Flex your interpersonal skills to translate the complexity of your work into tangible business goals Then "The Ideal Candidate is:" then bullet list of traits: - Customer first... - Innovative... - Creative... - A leader... - Technical... - Statistically-minded... - A data guru... Then "Basic Qualifications:" then list of bullet points (multiple lines): - Currently has, or is in the process of obtaining one of the following with an expectation that the required degree will be obtained on or before the scheduled start date: - A Bachelor's Degree in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field) plus 6 years of experience performing data analytics - A Master's Degree in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field) or an MBA with a quantitative concentration plus 4 years of experience performing data analytics - A PhD in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field) plus 1 year of experience performing data analytics - At least 1 year of experience leveraging open source programming languages for large scale data analysis - At least 1 year of experience working with machine learning - At least 1 year of experience utilizing relational databases Then "Preferred Qualifications:" then bullet list: - PhD in “STEM” field (Science, Technology, Engineering, or Mathematics) plus 3 years of experience in data analytics - At least 1 year of experience working with AWS - At least 4 years’ experience in Python, Scala, or R for large scale data analysis - At least 4 years’ experience with machine learning - At least 4 years’ experience with SQL Then "Capital One will consider sponsoring a new qualified applicant for employment authorization for this position." (maybe part of benefits? but not needed.) Then "The minimum and maximum full-time annual salaries for this role are listed below, by location." Then list of locations with salary ranges. Then "This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan." Then "Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website. Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level." Then other boilerplate: "This role is expected to accept applications for a minimum of 5 business days. No agencies please." etc. We need to remove cookie/navigation/legal/EEO/equal-opportunity/scam-warning boilerplate, duplicated lines, and application instructions. Thus we need to keep substantive content: description, team description, role description responsibilities, ideal candidate traits, basic qualifications, preferred qualifications, pay (salary ranges), benefits (mention of benefits), maybe schedule? Not present. No schedule info. So we should not create Schedule section. We need to use

for section headings: e.g., About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. We need to decide mapping. We have: - About the role: could be the introductory paragraphs (Data is at the center..., Team Description). Could combine. - Responsibilities: the "Role Description In this role, you will:" bullet list. - Requirements: maybe Basic Qualifications? Or Requirements could be basic qualifications. - Qualifications: maybe Preferred Qualifications? Or we could combine Basic and Preferred under Qualifications? The instruction says use headings for sections like Requirements, Qualifications, Skills, Benefits, Pay, Schedule. We need to decide. We have Ideal Candidate traits (soft skills). Could be under Skills? Or maybe under Qualifications? The Ideal Candidate is a list of attributes (customer first, innovative, creative, leader, technical, statistically-minded, data guru). Those could be considered Skills or Qualities. Could put under Skills. We have Basic Qualifications (education + experience). Could be under Requirements. We have Preferred Qualifications (additional). Could be under Qualifications. We have Benefits: mention of comprehensive benefits, performance incentive compensation. We have Pay: salary ranges. We have no Schedule. Thus produce sections: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay. We must not create empty sections. Now we need to format content. Use

for each heading. Under each heading, we need to use

for prose, and

• for any list of duties/requirements/benefits. Thus: - About the role: prose (maybe two paragraphs). Use

  . - Responsibilities: list of duties (the four bullet points). Use

  • . - Requirements: list of basic qualifications (the bullet points). Use
    • . Might need to include the sub-bullets for degree options. Could flatten: each option as separate
    • . Also the three experience requirements. - Qualifications: list of preferred qualifications (the bullet points). Use
      • . - Skills: list of Ideal Candidate traits. Use
        • . - Benefits: prose about benefits and incentive compensation. Could be

          . - Pay: list of salary ranges per location. Could be

          • each location with range. We must preserve all substantive information; do not invent or summarize away detail. Must not omit any detail. Thus we need to include all details from the posting. Let's extract exact text. About the role: combine first two paragraphs: "Data is at the center of everything we do. As a startup, we disrupted the credit card industry by individually personalizing every credit card offer using statistical modeling and the relational database, cutting edge technology in 1988! Fast-forward a few years, and this little innovation and our passion for data has skyrocketed us to a Fortune 200 company and a leader in the world of data-driven decision-making. As a Data Scientist at Capital One, you’ll be part of a team that’s leading the next wave of disruption at a whole new scale, using the latest in computing and machine learning technologies and operating across billions of customer records to unlock the big opportunities that help everyday people save money, time and agony in their financial lives. Team Description As part of the US Card data science organization, this team focuses on the customer management business, and builds top tier machine learning solutions to meet the various needs such as real-time customer behavioral prediction and valuation forecasts. We work in a team environment, collaborating closely with cross-functioning teams including the business, engineers, analysts, data science/quants, products." We need to ensure we keep the exact wording, but we can combine into paragraphs. Responsibilities: the four bullet points exactly as given: - Partner with a cross-functional team of data scientists, software engineers, and product managers to deliver a product customers love - Leverage a broad stack of technologies — Python, Conda, AWS, H2O, Spark, and more — to reveal the insights hidden within huge volumes of numeric and textual data - Build machine learning models through all phases of development, from design through training, evaluation, validation, and implementation - Flex your interpersonal skills to translate the complexity of your work into tangible business goals Requirements (Basic Qualifications): need to include all bullet points. First bullet: "Currently has, or is in the process of obtaining one of the following with an expectation that the required degree will be obtained on or before the scheduled start date:" then sub-bullets: - "A Bachelor's Degree in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field) plus 6 years of experience performing data analytics" - "A Master's Degree in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field) or an MBA with a quantitative concentration plus 4 years of experience performing data analytics" - "A PhD in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field) plus 1 year of experience performing data analytics" Then separate bullets: - "At least 1 year of experience leveraging open source programming languages for large scale data analysis" - "At least 1 year of experience working with machine learning" - "At least 1 year of experience utilizing relational databases" We need to preserve all. Qualifications (Preferred Qualifications): bullets: - "PhD in “STEM” field (Science, Technology, Engineering, or Mathematics) plus 3 years of experience in data analytics" - "At least 1 year of experience working with AWS" - "At least 4 years’ experience in Python, Scala, or R for large scale data analysis" - "At least 4 years’ experience with machine learning" - "At least 4 years’ experience with SQL" Skills (Ideal Candidate is): bullets: - "Customer first. You love the process of analyzing and creating, but also share our passion to do the right thing. You know at the end of the day it’s about making the right decision for our customers." - "Innovative. You continually research and evaluate emerging technologies. You stay current on published state-of-the-art methods, technologies, and applications and seek out opportunities to apply them." - "Creative. You thrive on bringing definition to big, undefined problems. You love asking questions and pushing hard to find answers. You’re not afraid to share a new idea." - "A leader. You challenge conventional thinking and work with stakeholders to identify and improve the status quo. You’re passionate about talent development for your own team and beyond." - "Technical. You’re comfortable with open-source languages and are passionate about developing further. You have hands-on experience developing data science solutions using open-source tools and cloud computing platforms." - "Statistically-minded. You’ve built models, validated them, and backtested them. You know how to interpret a confusion matrix or a ROC curve. You have experience with clustering, classification, sentiment analysis, time series, and deep learning." - "A data guru. “