Data Scientist Job Descriptions, Average Salary, Interview Questions

What Does a Data Scientist Do?

A Data Scientist is a professional who uses data to answer questions and make decisions. In order to extract valuable insights and inform business decisions, they collect, analyze, and interpret large and complex datasets.

Data Scientists draw conclusions from data using a variety of tools and techniques, including statistical analysis, machine learning, and data visualization.

Furthermore, they work closely with business stakeholders to understand their goals and use data to acheive them. Data Scientists are a combination of statistician, computer scientist, and subject matter expert who use their skills to empower the rest of their team.

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National Average Salary

Data scientist salaries vary by experience, industry, organization size, and geography. To explore salary ranges by local market, please visit our sister site

The average U.S. salary for a Data Scientist is:


Data Scientist Job Descriptions

Example 1

Our Data Science team is experimenting, testing, and driving major insights that impact a global network of clients. Excited? Come join us!

The Panel Analytics team builds and maintains a series of dashboards and analytical reports containing key performance metrics of the company’s Television Panel. We collaborate with other teams and determine what data and analytics can be used to best evaluate our methodology. This team is at the forefront of data science technology, exploring the best ways to build and automate pipelines, incorporate cyber security principles, and create powerful visualizations to illuminate and drive business decisions.


  • Learn and become an expert in the company’s Television Panel, with a focus on analyzing impacts to methodology and building, maintaining, and evolving dashboards to meet the ever changing landscape of media consumers
  • Responsible for research and analytics work to ensure our products and methodologies are working as expected in addition to exploring enhancements
  • Utilize tools such as Python, R, Tableau, and Spark to run analyses and create visualizations Analyze data, summarize results, and present research findings to audiences of varying levels of experience
  • Automate processes for recurring analyses, focusing on efficiency
  • Proactively identify data issues and trace the end-to-end process to determine the root cause
  • Identify gaps in current methodology and/or functionality and work with cross-functional teams to recommend and implement necessary fixes or future enhancements
  • Collaborating with methodology teams to drive end to end insights and own feedback circles for continuous improvement
  • Create clear documentation/presentation that explains ongoing trends in our Television panel
  • Address client and stakeholder questions in a timely manner

Is this the job for you

  • Data Scientist with a degree in business analytics, mathematics, statistics, computer science, or a related field and 0-2 years of experience with the following:
  • Producing data-driven insights Data Visualization experience, preferably via BI tools such as Tableau
  • Project management including working collaboratively across multiple teams
  • Summarizing insights and presenting research findings, often in the form of a compelling story
  • Coding in Python
  • Coding in SQL and/or Spark
  • Ability to communicate complex concepts to both technical and non-technical audiences

Example 2

In the data scientist role, you’ll join a product team and be a part of driving the vision, strategy, and process that enables the company’s Legal Plans to develop a best-in-class product and achieve its strategic growth plan.

You will be responsible for gathering, organizing and analyzing large and complex data sets and informing data-driven decision-making to improve our business. You will design, develop, and maintain customized reports using Tableau and other tools. You will also hunt for answers to complex business questions and proactively identify business drivers, insights, trends, problems, and opportunities. Further, you will assist the product team with financial guidance and effectively communicate with both technical and non-technical audiences at all levels.

Key responsibilities

  • Enhance data collection procedures by bringing together all relevant information
  • Process, cleanse, and validate the integrity of data to be used for analysis
  • Build models and analyze data to find insights and patterns
  • Design compelling visualizations, dashboards, reports, and presentations
  • Propose solutions and strategies to tackle business challenges
  • Track and assess financials for new and existing features
  • Perform ad hoc analysis and research for stakeholders
  • Assist project teams with financial guidance

Required essential business experience and technical skill

  • Bachelor’s degree in Computer Science, Mathematics, Statistics, or related field
  • At least 5 years of experience in a data science or data analyst-related role
  • Proven experience in Tableau
  • Proficiency in handling imperfect and complex data
  • Strong business acumen
  • Intellectual curiosity and drive
  • Ability to proactively and continuously identify insight and trends
  • Excellent communication, interpersonal, and presentation skills
  • Detail-oriented and able to perform in a high-pressure environment
  • Strong analytical thinking and problem-solving skills
  • Proficiency in Microsoft Office and SAP, Oracle, or related financial systems

Example 3

The Data Scientist position is for a curious and divergent thinker and is responsible for applying statistical, mathematic, and computational models to diverse sets of data to solve new problems and identify breakthrough opportunities. The role works to better understand consumer behavior, product assortment trends, channel purchase patterns, media, and marketing impacts to help shape the company’s strategy and execution. The Data Scientist is a key role on our Analytics and Insights team, which is part of our growing Digital organization.

Core accountabilities

  • Explores data to find breakthroughs
  • Deciphers what’s interesting and what’s important
  • Collaborates across diverse teams to launch analytics-based initiatives that improve consumer experience and lifetime value
  • Builds data science models for high priority use cases across the enterprise that help shape strategic decisions and future actions
  • Applies core analytical and statistical methods, including regression, segmentation and clustering, predictive modeling, time-series analysis, and machine learning techniques
  • Conducts iterative experiments and simulations to understand the probability of outcomes, and interprets the results into impactful conclusions
  • Creates a more knowledgeable company by communicating discrete learnings to working teams, and by leveraging data visualization platforms to drive change at scale
  • Ensures data quality and governance, in partnership with Information Technology teams and external partners
  • Keeps current with relevant research and novel data science methods

Core competencies

  • Drive for Results
  • Customer Focus
  • Collaborate Cross Functionally
  • High Integrity


  • Master’s degree preferred in Mathematics, Statistics, Computer Science, Business Management, or similar field or experience
  • 1-2 years of experience in Data Science, Analytics, Business Intelligence, or similar field
  • Fluent in R, and Python; experience with SQL and data visualization platforms
  • Exceptional relationship management experience. Must be extremely detail-oriented and have a hands-on approach
  • Ability to thrive in a multi-tasked, fast-paced environment with frequently changing priorities
  • Strong communication skills, both written and oral; ability to communicate analytical conclusions with leadership effectively

Sample Interview Questions

  • Can you explain linear regression?
  • Can you explain logistic regression?
  • Can you define a confusion matrix?
  • What’s the difference between true positive and false positive rates?
  • What do you consider to be the key differences between data science and traditional application programming?
  • What’s the difference between supervised and unsupervised learning?
  • What are popular libraries used in data science?
  • How would you go about pruning in a decision tree algorithm?
  • What’s an RNN?
  • What’s an ROC curve?
  • How would you explain the difference between data modeling and database design?

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