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Lead, Spend Data Analytics

Summary

The Lead, Spend Data Analytics is responsible for implementing the design, development, deployment, and maintenance of predictive, prescriptive, and statistical models for Procurement savings and cash flow initiatives, pipeline opportunities, risk mitigation, and supplier development activities. The incumbent will bring an expertise in forecasting, optimization, data mining, analysis, and analyzing complex datasets. The Lead will conduct studies with the use of descriptive and supervised machine learning methods and advanced statistical methods using innovative and the latest advanced technique and algorithms. Additional responsibilities include summarizing concise and professional reports and presentations of findings to a variety of internal clients, as well as working with other departments to achieve the overall company objectives.

Duties and Responsibilities

  • Collect, consolidate, cleanse and preprocess data from various internal and external sources to ensure quality and reliability.
  • Explore and visualize data to identify patterns, trends, and relationships.
  • Create and execute machine learning algorithms and statistical models.
  • Data Interpretation: Use programming, mathematical, statistical, and analytical skills, to interpret data and format for use within Procurement.
  • Insight Extraction and Recommendations: Based on interpretation, be able to provide project related scenarios with initial recommendations.
  • May be asked to provide tailor-made solutions, based on data to help Procurement/Medline achieve unique objectives and goals.
  • Reporting and Proposals: Prepare reports outlining business forecasts, estimates or proposals based on data analysis, as needed.
  • Lead cost optimization and productivity initiatives and opportunity searches. This involves analyzing spending data, commodity supply trends, and market intelligence to identify cost-saving measures.
  • Create and evaluate cost models based on financial data and industry research. This includes extracting key information essential for management decision-making and external benchmarking.
  • Provide negotiation support and data insights for negotiations and RFx (Request for Proposal/Quote) processes to support Sourcing and Procurement teams.
  • Drive operational excellence for data usage, storage supporting best practices to reduce costs and improve financial performance.
  • Management of data needs, and analytics for Spend Management Function

Salaries vary by location and are based on numerous factors, including, but not limited to, the relevant market, skills, experience, and education of the selected candidate. If an estimated salary range for this role is available, it will be provided in our Target Salary Range section. Our compensation package also includes bonus eligibility and a comprehensive benefits program. Benefits information can be found at Sidley.com/Benefits.

Target Salary Range

$146,000 - $160,000 if located in Illinois

Qualifications

To perform this job successfully, an individual must be able to perform each Essential Duty and Responsibility (the “Essential Duties”) satisfactorily.  The requirements listed below are representative of the minimum knowledge, skill, and/or ability required.  Reasonable accommodations may be made to enable individuals with disabilities to perform the Essential Duties.  If you need such an accommodation, please email staffrecruiting@sidley.com (current employees should contact Human Resources).

 

Education and/or Experience: 

Required:

  • Bachelor's degree in a quantitative field such as  statistics, applied mathematics, financial mathematics, computer science, engineering, operations research, or a related field.
  • A minimum of 5 years of experience
  • Experience in client-facing roles
  • Advanced statistical programming proficiency with R or Python
  • Experience with other programming and data manipulation languages (SQL, Hive, Spark, C/C++, Java, Ab Initio)
  • Effective communication at multiple levels within and outside the organization
  • Strong analytical, critical thinking, and creative problem solving skills, with evidence of initiative-taking
  • Project and time management skills with the ability to manage multiple assignments effectively
  • Excellent communication skills with an ability to translate complex financial models into simple, understandable concepts
  • Ability to simplify strategy in executable phases – translating strategy to execution is critical

Preferred:

  • Master's Degree in Data Science, Statistics, Mathematics, Computer Science, or Engineering
  • Proven ability to operate independently in a complex and dynamic, matrixed environment
  • Familiarity with operations and procurement data and solutions
  • Strong relationship building capabilities
  • Knowledge of IT Systems, AI influence in today’s processes

Other Skills and Abilities:

The following will also be required of the successful candidate:

  • Strong organizational skills
  • Strong attention to detail
  • Good judgment
  • Strong interpersonal communication skills
  • Strong analytical and problem-solving skills
  • Able to work harmoniously and effectively with others
  • Able to preserve confidentiality and exercise discretion
  • Able to work under pressure
  • Able to manage multiple projects with competing deadlines and priorities

Sidley Austin LLP is an Equal Opportunity Employer

Average salary estimate

$153000 / YEARLY (est.)
min
max
$146000K
$160000K

If an employer mentions a salary or salary range on their job, we display it as an "Employer Estimate". If a job has no salary data, Rise displays an estimate if available.

What You Should Know About Lead, Spend Data Analytics, Sidley Austin LLP

Are you passionate about data and analytics? Sidley Austin LLP is on the lookout for a Lead, Spend Data Analytics in Chicago, and this role might just be your next big adventure! As the Lead, you'll take charge of designing, developing, deploying, and maintaining sophisticated predictive models to drive Procurement savings and cash flow initiatives. Your expert skills in forecasting and data mining will be put to the test as you analyze complex datasets and provide intelligent insights to various internal teams. You will harness advanced machine learning methods and statistical techniques to create innovative solutions for cost optimization and risk mitigation. Beyond the numbers, you'll be a key player in crafting concise reports and presentations to communicate your findings effectively. Collaboration is key, and you'll work closely with other departments to help the company reach its goals. With responsibilities ranging from data cleansing and visualization to providing negotiation support and leading productivity initiatives, every day will bring new challenges! If you're an analytical thinker who thrives in a dynamic environment, this is the opportunity for you to shine and make a measurable impact within a stellar firm!

Frequently Asked Questions (FAQs) for Lead, Spend Data Analytics Role at Sidley Austin LLP
What are the responsibilities of the Lead, Spend Data Analytics at Sidley Austin LLP?

The Lead, Spend Data Analytics at Sidley Austin LLP is tasked with implementing predictive and statistical models focused on Procurement savings. Key responsibilities include data collection and purification, running machine learning algorithms, interpreting complex datasets, and creating insightful reports. Additionally, the lead will guide cost optimization initiatives, analyze spending data, and provide valuable insights for negotiations and proposals.

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What qualifications are required for the Lead, Spend Data Analytics role at Sidley Austin LLP?

To qualify for the Lead, Spend Data Analytics position at Sidley Austin LLP, candidates must hold a bachelor's degree in a quantitative field and possess at least 5 years of experience. Proficiency in statistical programming languages like R or Python is essential, along with familiarity with SQL, Spark, and other data manipulation languages. Strong communication skills, project management abilities, and a proven analytical mindset are also necessary to succeed in this role.

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What skills will help me succeed as a Lead, Spend Data Analytics at Sidley Austin LLP?

Success as a Lead, Spend Data Analytics at Sidley Austin LLP requires strong analytical and critical thinking skills, along with effective communication abilities. A robust understanding of statistical methods, machine learning, and data visualization techniques will be indispensable. Interpersonal skills for building relationships across departments and the capacity to manage multiple projects with competing deadlines are also essential for this role.

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What does the salary range look like for the Lead, Spend Data Analytics position at Sidley Austin LLP?

The estimated salary range for the Lead, Spend Data Analytics position at Sidley Austin LLP, located in Illinois, is between $146,000 and $160,000. In addition to base salary, the compensation package includes bonus eligibility and a comprehensive benefits program that adds exceptional value to your employment.

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What kind of environment does Sidley Austin LLP provide for the Lead, Spend Data Analytics role?

Sidley Austin LLP fosters a dynamic and collaborative work environment for its Lead, Spend Data Analytics role. The firm encourages innovation and collaboration, providing resources to leverage cutting-edge data analytics techniques. Employees benefit from a supportive culture that values initiative and offers opportunities to work on impactful projects across various departments.

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Common Interview Questions for Lead, Spend Data Analytics
Can you explain your experience with predictive modeling in data analytics?

When answering this question, highlight your specific experiences with predictive modeling. Discuss the types of models you've created, the data used, and any measurable outcomes your models achieved. Illustrate your understanding of when to apply different algorithms based on the problem at hand.

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How do you handle data cleaning and preprocessing?

In your response, explain your methodologies for data cleaning and preprocessing, emphasizing attention to detail and steps taken to ensure data quality. Mention specific tools or programming languages you use, and provide examples where this process led to more reliable analytics or insights.

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What machine learning algorithms are you proficient in, and how have you applied them?

Discuss your familiarity with various machine learning algorithms in your answer. Mention specific projects where you applied these algorithms, focusing on the context, your role, and the results. Be sure to emphasize your problem-solving approach and results achieved.

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How do you prioritize multiple projects and deadlines?

Provide a structured approach in your answer, such as using project management tools or methodologies (like Agile or Kanban). Highlight your ability to assess project urgency versus importance and how you effectively communicate with stakeholders to manage expectations.

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Can you describe a time when you provided key insights from data analysis?

Share a specific example where your data analysis influenced a business decision. Focus on the insights you extracted, how you presented them to stakeholders, and what impact they had on the organization. This demonstrates your ability to translate complex data into actionable strategies.

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What tools do you prefer for data visualization and why?

Identify your preferred data visualization tools (like Tableau, Power BI, or custom Python libraries) and explain why you find them effective. Share examples of how you’ve used these tools to communicate data findings clearly and effectively to non-technical audiences.

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How do you keep yourself updated with the latest trends in data analytics?

Mention specific resources such as online courses, webinars, or industry publications you engage with to stay informed about trends in data analytics. Highlight your commitment to continuous learning and how you apply new knowledge to your work.

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What is your approach to statistical analysis in your projects?

Discuss your methodology for conducting statistical analysis, including hypothesis testing, regression analysis, or A/B testing. Share examples of projects where your statistical insights made a significant difference in the results or decision-making process.

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How have you collaborated with teams in your previous roles?

Provide examples of cross-functional collaboration, detailing how your analytics expertise supported team initiatives. Highlight your communication skills, and how you navigate different perspectives to achieve common objectives effectively.

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What strategies do you use for cost optimization in data analysis?

Outline how you analyze spending data and market trends to identify cost-saving opportunities. Mention specific analyses you've performed in the past that led to successful cost optimization, illustrating your ability to deliver measurable results.

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DATE POSTED
March 29, 2025

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