Business Intelligence Analyst / Data Scientist

As part of our Analytics team, this data and analytics professional helps our business leaders understand how and why we are successful and where we can improve.

They are business savvy, think critically, know how to query and manipulate data, and apply advanced analytical methods and statistics.

They work with our data engineers to develop rich datasets that make it easy to answer pertinent questions. They partner with business teams to turn insights into better day-to-day decisions through reports, models, and automation.

Are you ready to apply Make sure you understand all the responsibilities and tasks associated with this role before proceeding.

JOB RESPONSIBILITIES

Product & Initiative Leadership

Identify different options to solve for requested features or analyses and propose solutions considering trade-offs on cost, scalability, supportability, and usability.

Performance Expectations

• Regularly weighs several factors beyond the analytics realm when recommending solutions or approaches. Deeply understands the implementation context and user perspective.
• Has a sufficiently large toolkit of prior experience and understanding of analytical approaches to generate alternative ideas.

Reporting & Analytics

Conduct exploratory analysis needed to substantiate or frame analytics solutions for major business questions or product features.

Includes the use of non-governed data.

• Analyze business questions and communicate findings through briefs, presentations, reports or other media. Clarify and document the questions, how findings may be used, and the analysis approach.
• Develop complex deterministic models as well as probabilistic models enabling the business to make decisions considering potential and likely outcomes.
• Conduct impact analysis of business performance, identifying an appropriate approach to break down and explain performance based on internal and external contributing factors, trends and patterns.
• Develop reports and dashboards for an entire problem space, such as website performance, lead conversion or studio portfolio analysis.

These are product features that are supported and evolved over time as opposed to one-off analyses.

• Develop end-user applications with low-code tools to surface actionable analytics insights, collect data or execute analytics-driven workflows / actions.
• Work with data engineers to create new datasets, providing requirements and use cases, and jointly analyzing the suitability of source data.

Performance Expectations

Uses a toolkit of analytical methods that includes (when appropriate!) basic statistical concepts, such as statistical significance, distributions, independent and dependent probabilities, confidence intervals etc.

to answer business questions.

• Conveys to business stakeholders how to apply probabilistic answers to their business decisions.
• Able to ask questions to fully understand the context and business question asked and how answering it will make a difference.
• Identifies key assumptions around perceived or real business problems and validates them before proceeding with the analysis.
• Makes accurate inferences from data. Knows what the data says and what it doesn’t say and is transparent about shortcomings of the data.

Suggests further analysis paths if answer is not sufficiently conclusive.

• Demonstrates a strong understanding of predictive analytics, prescriptive analytics, machine learning algorithms, advanced modeling evaluation, & data visualization.
• Shows interest and can speak the language of the business domain they are supporting (e.g., marketing, finance, operations).

When presenting findings business stakeholders follow.

• Is recognized by business stakeholders as their analytics partner whom they can rely on for developing data-informed insights.
• Participates in creating reporting and analytics standards, identifying, coaching, and teaching analysis methods. Is sought out by other analysts for advice.
• Knows the principles of dataset design and independently works with data engineers to ensure the datasets from domain data to published datasets will meet all current or planned use cases.

REQUIRED QUALIFICATIONS / EXPERIENCE

Bachelor’s degree in computer science, data science, statistics, mathematics, business administration, economics, or related field with a focus on logic and quantitative analysis.

MBA or experience in or with specific business functions is a plus.

• 4 - 6 years of experience in reporting and analysis. Can demonstrate how to effectively communicate findings to stakeholders outside of the field of analytics.
• Work examples of developing probabilistic prediction models or analyzing complex questions using advanced methodologies.
• Applied statistical analysis and can explain how and why it was used to further the insights gained.
• Intermediate-level proficiency in querying and manipulating data using SQL, including analytic functions. On the job experience with Python, R or similar languages.

Preferred Qualifications

• Previous experience practicing a variety of regression analysis methods (i.e. linear, polynomial, time series, etc.)
• Previous experience identifying causation through a variety of methods (i.e. Randomly Controlled Trial (RCT), Difference-in-Differences (DID), etc.)
• Familiarity towards establishing correlation from various perspectives (Rank, Coefficient, Tau, etc.)

SKILLS & ABILITIES

• Able to explain several common biases that affect what questions are asked, which data is looked at and how results are interpreted.
• Able to contrast meaningful and poor metrics or KPI’s.
• Well organized. Thinks about how to do the work, then acts. Seeks to contextualize the work.
• Good interpersonal skills, seeks to understand others’ viewpoints, motivations, ways of thinking.
• Respectfully challenges others’ approach or viewpoints when making inferences from data that are not logically or methodologically sound.
• Shows curiosity about the industry and can explain how Orangetheory is differentiated.
• Can articulate Orangetheory’s goals and strategic imperatives.

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