Data Scientist Job Description: Top Duties and Requirements

A Data Scientist is responsible for collecting, analyzing, and interpreting data sets for a company to provide data-driven solutions for business decisions. The duties of Data Scientists include developing custom data models, visualizations, and algorithms to interpret data, assessing the accuracy of new data and data-gathering strategies, and implementing and developing tools to monitor and analyze data accuracy and performance.

 

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Data Scientist duties and responsibilities

Data Scientists handle many aspects of a company’s data, and they work with many teams and individuals within an organization. Key responsibilities of a Data Scientist may include:

  • Mining and analyzing data from company databases to improve marketing, product development, or overall business strategies
  • Using predictive modeling to optimize revenue growth, marketing campaigns, and customer experiences
  • Developing A/B testing campaign frameworks and testing model quality
  • Communicating and coordinating with teams to implement data-driven strategies while measuring progress

 

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Data Scientist Job Description Examples:

 

Example 1

About the Role The Analytics team is looking for experienced Data Scientists to guide measurement, strategy, and tactical decision-making as we expand our logistics platform into new countries. We are looking to hire in several countries across a variety of teams and levels to accelerate growth in our current international markets (Canada and Australia) and lead the strategy and execution of new market launches (more coming!) Data Scientists at DoorDash work to uncover insights and turn them into relevant recommendations, driving decisions for the entire organization. Analytics is integral to all operational areas at DoorDash. As a Data Scientist at DoorDash, you'll use your quantitative background to mentor other scientists and dive into large datasets to guide decision-making. We tackle a multitude of exciting challenges including customer acquisition, balancing supply and demand, fraud and support, marketing, marketplace efficiency, and more. If you enjoy finding patterns amidst chaos, are excited to build a market from 0 to 1, and have experience using analytics to affect revenue, growth, operations or beyond, we’re looking for someone like you! You’re excited about this opportunity because you will… Use quantitative analysis and the presentation of data to see beyond the numbers and understand what drives our business Build full-cycle analytics experiments, reports, and dashboards using SQL, R, Python, or other scripting and statistical tools Produce recommendations and use statistical techniques and hypothesis testing to validate your findings Provide insights to help business and product leaders understand marketplace dynamics, user behaviors, and long-term trends Identify and measure levers to help move essential metrics and make recommendations Work backwards from understanding and sizing problems to ideating solutions Report against our goals by identifying essential metrics and building executive-facing dashboards to track progress Be excited to travel (when it’s safe!) to meet with business partners and the team in each market We’re excited about you because you have… A degree in Math, Physics, Statistics, Economics, Computer Science, or similar domain 5+ years of experience in data analytics, consulting, or related quantitative role Experience working with funnel optimization, user segmentation, cohort analyses, time series analyses, regression models, etc Expertise of SQL queries, ETL, A/B Testing, and statistical analysis [website] hypothesis testing, experimentation, regressions) with statistical packages, such as Matlab, R, SAS or Python Proficiency in one or more analytics & visualization tools [website] Chartio, Looker, Tableau) The insight to take ambiguous problems and solve them in a structured, hypothesis-driven, data-supported way The determination to initiate and lead projects to completion in a scrappy environment Prior experience working abroad or in international expansion preferred but not required Fluent English required, proficiency in additional languages a plus Why You’ll Love Working at DoorDash We are leaders - Leadership is not limited to our management team. It’s something everyone at DoorDash embraces and embodies. We are doers - We believe the only way to predict the future is to build it. Creating solutions that will lead our company and our industry is what we do - on every project, every day. We are learners - Everyone here is continually learning on the job, no matter if we’ve been in a role for one year or one minute. We are - Our mission is to grow and empower local economies. We are committed to our customers, merchants, and dashers and believe in connecting people with possibility. We are all DoorDash - The magic of DoorDash is our people, together making our inspiring goals attainable and driving us to greater heights. We offer great compensation packages and comprehensive health benefits. About DoorDash DoorDash is a technology company that connects customers with their favorite local and national businesses in all 50 US states, Canada, and Australia. Founded in [phone number], DoorDash empowers merchants to grow their businesses by offering on-demand delivery, data-driven insights, and better in-store efficiency, providing delightful experiences from door to door. By building the last-mile delivery infrastructure for local cities, DoorDash is bringing communities closer, one doorstep at a time. Read more on the DoorDash Engineering blog or at Our Commitment to Diversity and Inclusion We’re committed to growing and empowering a more inclusive community within our company, industry, and cities. That’s why we hire and cultivate diverse teams of the best and brightest from all backgrounds, experiences, and perspectives. We believe that true innovation happens when everyone has room at the table and the tools, resources, and opportunity to excel.

Example 2

*A special note from Mark Hobbs, CEO and Rachel Crosbie, VP Strategy and Operations: * We encourage those of diverse backgrounds and/or underrepresented groups to apply to our jobs even if the qualifications are not an exact match. Fundmetric is a team willing to invest in your professional development. Fundmetric strives to create a diverse and inclusive team. There is an added emphasis on achieving this at Fundmetric because it is mission-critical to remove bias from our artificial intelligence and the products and services we offer. Please feel free to address any special circumstances in your covering letter or contact us, if the application process itself presents a barrier. *Data Scientist at Fundmetric* Fundmetric is a data generation ecosystem, designed to help nonprofits transform their fundraising. Our AI-powered platform uses machine learning to predict donor behavior and identify future leadership opportunities, driving more frequent and higher donations. We work with organizations across the nonprofit sector, focusing on educational institutions, hospitals & health providers and general non-profit. The iteration of our platform is driven by our clients. *Objective: * We are looking for a data scientist that will help us discover the information hidden in vast amounts of data, and help us make smarter decisions to deliver even better products. Your primary focus will be in applying data mining techniques, doing statistical analysis, and building high quality prediction systems integrated with our products. This should include understanding of platforms such as Tensorflow and framework such as Pandas. *Responsibilities* · Selecting features, building and optimizing classifiers using machine learning techniques · Data mining using methods · Extending company’s data with third party sources of information when needed · Enhancing data collection procedures to include information that is relevant for building analytic systems · Processing, cleansing, and verifying the integrity of data used for analysis · Doing ad-hoc analysis and presenting results in a clear manner · Creating automated anomaly detection systems and constant tracking of its *performance* *Skills and Qualifications* · Doctorate in Computer Science preferred · Excellent understanding of machine learning techniques and algorithms, such as k-NN, Naive Bayes, SVM, Decision Forests, etc. · Experience with common data science toolkits, such as R, Weka, NumPy, MatLab, etc *To Apply*: C.V, References and a covering email may be sent to: Mark Hobbs, CEO via his assistant [website] Job Type: Full-time Salary: per year

What does a Data Scientist do?

Data Scientists are hired to support product, leadership, sales, and marketing teams with information gained from company data. They use large data sets collected from the company’s data sources to analyze and develop solutions to address business challenges. Typically, Data Scientists understand how to use data tools, analyze data sets, build and implement models and visualizations from these data sets, and create algorithms to communicate to stakeholders and other vital teams within the business.

 

Data Scientist skills and qualifications

Data Scientists need a wide variety of skills to handle the responsibilities of the position. The most important ones to include in a job description are: 

  • Oral and written communication skills
  • Problem-solving skills
  • Ability to create data architectures
  • Knowledge of machine learning techniques
  • Knowledge of advanced statistical analysis techniques and concepts, and how to apply them to data sets
  • A desire to learn new techniques and technologies
  • Flexibility to work as a member of the team and as an individual contributor

 

Data Scientist experience requirements

The exact amount of experience required for a Data Scientist position varies based on the responsibilities of the job. Senior Data Scientists, for example, usually have a minimum of five to seven years of experience building statistical models to communicate to teams and stakeholders. Entry-level Data Scientists, meanwhile, can qualify with a data science boot-camp certification or some internship experience. 

Data Scientists also have experience working with different programming languages such as C, C++, and Java, along with experience in statistical techniques such as Random Forest, Boosting, social network analysis, text mining, and generalized linear models. Other experience include statistical computer languages such as R, SQL, and Python, web services like Spark and Redshift, and machine learning algorithms like regression, simulation, modelling, decision trees, and neural networks.

 

Data Scientist education and training requirements

Data Scientists typically have a master’s or doctorate degree in mathematics, computer science, statistics, or other quantitative fields. Data Scientists may also have certifications or continuing education in different tools and technologies such as programming languages, data analysis tools, machine learning, the Internet of things, artificial intelligence, or other related technologies.

 

Data Scientist salary expectations

According to Indeed Salaries, the average salary for a Data Scientist is $81,700 per year. Wages may be dependent on factors including relevant experience, location, education level, and company.

 

Job description samples for similar positions

If a Data Scientist isn’t the position you’re looking for, other job description samples are available for related or similar job positions:

 

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Data Scientist job description FAQs

 

What is the difference between a Data Scientist and a Data Analyst?

Both Data Scientists and Data Analysts require statistical knowledge, an understanding of algorithms, communication skills, and software engineering skills, but there are significant differences between the job responsibilities. Data Analysts work directly with the data, and they focus on using programming languages to dissect data and solve business problems. A Data Scientist focuses on the overall picture of data and understands analysis, modelling, math, statistics, and computer science, allowing them to create visualizations and models to communicate the data findings to teams and stakeholders. Data Scientists also have some business acumen and they understand how data relates to business operations.

 

What qualities make a good Data Scientist?

Talented Data Scientists should demonstrate a sharp business acumen and an understanding of how raw data translates to data-driven business decisions to improve sales, marketing, product development, or customer experience. Ideal candidates should also have a passion for analyzing data, developing statistical models, and communicating this knowledge to teams and stakeholders. It is also important to have a Data Scientist who can communicate high-level technology concepts in an approachable way for the layperson.

 

What types of businesses need Data Scientists?

Data Science is a growing field that is necessary to virtually every industry. Data Scientists are needed in the retail and hospitality industries to analyze customer data, in the finance sector for financial insights, and in healthcare for disease modelling, improvement of patient experience, and the implementation of wearable healthcare devices. Other industries, such as construction and transportation, employ Data Scientists to analyze patterns such as material-based expenses, time frames, and traffic routes, among others.

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