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3 Data Scientist Job Description Samples & Guide (2022)

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Stephen Greet, Co-founder

February 25, 2022

Data science is the profession that utilizes scientific methods, processes, algorithms, and systems to identify and extract data to transform into useful information and business insights from very large volumes of structured and unstructured data. Data scientists apply knowledge and expertise from across a broad range of fields and technical domains.

The profession of data science is continuing to grow and is a very in-demand role. A good data professional knows they must advance past the traditional data analysis skills, such as analyzing large amounts of data, data mining, and programming to find the most crucial intelligence for their organizations. To most efficiently process and achieve maximum benefits at each step of the data science lifecycle, they must have top-notch expertise across the spectrum of data gathering and analysis.

Data scientists typically work for corporations across all industries to provide them with meaningful insights about how to improve their business operations. They use their knowledge of coding and other computer programming tactics to automate data collection and storage tasks. Their job is to work closely with company departments to pull data about business decisions or initiatives to objectively measure their success. They are also responsible for creating systems for collecting and storing data within company databases.

Attracting an exceptional data scientist for your organization can be a difficult task, but we’ll give you some great examples and guidance to create the best data scientist job description that will bring the right people to you.

Data Scientist Job Description Example

The job: Gather Inc. is a thriving real estate enterprise headquartered in Phoenix, Arizona. 

We are seeking a data scientist to build, train, and integrate machine learning models for demand-based rent pricing and predictive analytics across a large portfolio of single-family homes. 

This role will also partner with engineering leaders to develop back-end infrastructure to support the scaling of ML-based business intelligence assets. As a data scientist working for us, you will shape our growth and future business strategies. By applying your technical skills, analytical mindset, and intuition to huge, rich data sets, you will help define the direction we go in our management of billions of dollars in assets around the world.

What you’ll be doing: Our data scientist position is a very important role in our firm. You will be analyzing and modeling critical data that will drive our business strategies and enable us to make decisions that will make or break our profitability.

  • Translate business needs into seamless ML-based tools supporting real-time and automated decision-making.
  • Develop infrastructure and algorithms supporting single-family home rental rate optimization based on real-time rates of comparison.
  • Build ML models and collaborate with DevOps engineers to implement the models in production.
  • Improve data quality and pricing model accuracy based on pricing and demand (price elasticity and lease signings).
  • Launch and scale pricing systems across a portfolio of single-family communities.
  • Collaborate on data pipeline strategy and roadmap development for model training, real-time pricing, and continuous model improvement, leveraging stakeholder input and market response to pricing changes.

Benefits: We are committed to offering competitive salaries and a comprehensive benefits package that provides options for both you and your family’s health, wellness, and financial security.

  • Job type: full-time, onsite
  • Salary: $89,000–132,000/year
  • Additional compensation: bonuses and profit sharing
  • Insurance: health, dental, vision, life
  • Retirement: 401(k)

Qualifications: 

  • Master's degree in statistics or related field.
  • 5+ years combined experience in software development and data science
  • Experience with data science and machine learning tools, including Jupyter Notebooks, TensorFlow (or other), NumPy, Pandas, Scikit-learn, etc.
  • Expertise in Python and querying languages (e.g. SQL)
  • Familiarity with Java
  • Strong expertise in training, pruning, and maintaining ML models in TensorFlow and/or PyTorch
  • Knowledge of assembling and preparing data sets and conducting feature engineering
  • Experience with clustering and training random forests and deep networks
  • A deep understanding of common families of models, feature engineering, feature selection, and other practical machine learning techniques
  • Familiarity with the architecting, building, testing, and scaling of resilient back-end services
  • Experience with modern data pipelines (Open Source and data engineering technologies) and in training deep networks (CNN, etc.) for computer vision applications
  • Management or direction of large international teams of third-party developers

About the company: Founded in 2009, we have grown rapidly and have a portfolio in excess of $5 billion in assets. We purchase, sell, and manage a huge collection of properties. Our business services also include loan servicing for both residential and commercial properties. Examples of key assets in our residential real estate portfolios include undeveloped land, houses, condominiums, and townhouses.

Senior Data Scientist Job Description Example

The job: Whats Amattau Limited is a growing nonprofit that provides healthcare services and outreach to small and underserved communities. Our data scientist role is perfect for someone with a passion for seeking and discovering useful information from vast amounts of seemingly arbitrary and random data. This job is also for someone who cares about serving people. We need someone who can make an immediate impact by taking the initiative to build a data science team and create a set of processes and procedures to enable us to build successful services for our communities. 

What you’ll be doing: The main objective of this role is to institute a group to assess vast quantities of data regarding the demographics, healthcare statistics, and the medical industry as they relate to our markets. You will work with our business teams to create KPIs and tools to evaluate our processes and services.

  • Collect data from established channels and through means such as analyzing business results or the creation of new studies.
  • Create new, experimental frameworks to collect data, and build tools to automate data collection.
  • Carry out preprocessing (structured and unstructured data), cleansing, and validating the integrity of data to be used for analysis.
  • Mine and analyze data from company databases to drive optimization and improvement of service development, marketing techniques, and business strategies.
  • Correlate similar data to find actionable information and present results concisely.
  • Collaborate with stakeholders throughout the organization to identify opportunities for leveraging company data to drive business solutions.
  • Assess the effectiveness and accuracy of new data sources and data gathering techniques.
  • Develop custom data models and algorithms to apply to data sets.
  • Use predictive modeling to increase and optimize customer experiences, revenue generation, ad targeting, and other business outcomes.
  • Institute company A/B testing framework and test model quality.
  • Create processes and tools to monitor and analyze model performance and data accuracy.
  • Leverage machine learning tools to select features and create and optimize classifiers.
  • Enhance data collection procedures to include all relevant information for developing analytic systems.
  • Build prediction systems and machine learning algorithms.

Benefits: We offer a competitive compensation plan with salary and bonus, health insurance, 401(k), option plan, and unlimited vacation policy.

  • Job type: full-time (Central Standard Time), some travel (10%) required
  • Salary: $122,000–168,500/year
  • Additional compensation: bonuses
  • Insurance: health, dental, vision, life
  • Retirement: 401(k)

Qualifications: 

  • A master’s degree in computer science, social science, mathematics, statistics, or engineering
  • Outstanding technical writing and presentation skills to communicate complex data in simple, actionable ways
  • Ability to visualize data in the most effective way possible for a given project or study
  • Exceptional analytical and problem-solving skills
  • A drive to learn and master new technologies and techniques
  • Works independently and with team members with diverse backgrounds
  • Knowledge of advanced statistical techniques, concepts, and applications (regression, properties of distributions, statistical tests, proper usage, etc.)
  • Experience using statistical computer languages (R, Python, SLQ, etc.) to manipulate data and draw insights from large data sets
  • Working knowledge of a variety of advanced machine learning techniques (regression, simulation, scenario analysis, modeling, clustering, decision tree learning, artificial neural networks, etc.)
  • Coding experience with several languages: C, C++, Java, JavaScript, etc. (preferred)
  • Expertise with statistical and data mining techniques: GLM/Regression, Random Forest, Boosting, Trees, text mining, social network analysis, etc.
  • Experience analyzing data from third-party providers: Google Analytics, Site Catalyst, Coremetrics, Adwords, Crimson Hexagon, Facebook Insights, etc.

About the company: Whats Amattau Limited was started in 2012 in scenic Spokane, Washington, and we are expanding our coverage areas and services. We rely heavily on healthcare and demographic data analysis, as well as focus on leveraging technology and big data to drive growth. We are a very passionate, driven, and highly team-oriented group looking for a like-minded data science professional to join our team.

NLP Data Scientist Job Description Example

Job details: Flying High Airlines is a charter airline company that caters to Fortune 50 executives, high rollers, and celebrities. Established in 2007 and headquartered in beautiful Agoura Hills, California, we need a talented Natural Language Processing (NLP) data scientist to drive our online strategy and develop a market-leading intuitive system based on data analysis to focus our UI developers and marketing teams. This function is a key role within our company that works directly with our executive management team, providing mission-critical advisement.

Role: This position requires equal aptitude and enthusiasm in executing these critical functions. 

  • Build and implement advanced machine learning models for flight arrival and departure prediction applications.
  • Research, develop, and evaluate advanced text speech and ATI Messaging processing algorithms and implementations.
  • Utilize Machine Learning (ML) technology for speech processing and air transport messages processing.
  • Prototype and build algorithms and advanced machine learning techniques for applications such as speech modeling, QAR data modeling rendering, speech recognition, blind source separation, and speaker ID.
  • Stay up to date on studies and prototypes of new technologies, and demonstrate proactivity in the data science and technology communities.
  • Create innovative solutions in areas such as machine learning, computational linguistics, NLP, advanced and semantic information search, extraction, induction, classification, and exploration.
  • Develop and maintain the client and NLP pipeline for document data extraction semantics and sentiment processing and understanding.
  • Create products that provide a great user experience along with high performance, security, quality, and stability.
  • Work with stakeholders throughout the organization to identify opportunities to better leverage company data for driving optimized business solutions.

Benefits: We have strong competitive compensation plans with salary and bonus, full benefits, including health insurance, 401(k), option plan, and unlimited vacation policy.

  • $125,000–170,000 per year
  • Annual performance bonuses
  • Full coverage health insurance (vision, dental, life)
  • Onsite gym facility with wellness and diet assistance programs available
  • Special programs with season passes for local sporting and cultural events
  • 401(k) and profit-sharing plan

Qualifications: 

  • Master’s or Ph.D. in statistics, mathematics, computer science, artificial intelligence, machine learning, or another quantitative field
  • 5+ years of professional experience as a data scientist or in a related role
  • 3+ years of work experience in voice-related projects
  • 3+ years of work experience in software development
  • Experience establishing supervised and unsupervised learning Client/NLP models, including data cleaning, data analytics, feature creation, model selection and ensemble methods, performance metrics, and visualization
  • Knowledgeable of signal processing techniques, including adaptive filtering, filter banks and wavelet processing, speech analysis and synthesis, and speech and audio coding
  • Experienced in text classification topic mining, speech enhancement, and speech/audio coding and compression
  • Hands-on experience with feature extraction techniques (GMM, HMM, NMF/spectrograms, MFCC, etc.) for voice recognition and audio event classification.
  • Solid understanding of developing and creating real-world AI/ML applications such as prediction, recommendation, computer vision, bots, NLP, sentiment, knowledge, content intelligence, etc.
  • Deep knowledge of popular ML frameworks such as TensorFlow, PyTorch Keras, Spark ML, Scikit-learn, XGBoost, H2O, etc.
  • Design and data architecture experience at multiple levels (high-level to detailed) and across multiple views (conceptual, logical, physical, data flow, and sequence diagrams)
  • Hands-on architectural guidance and leadership through the entire lifecycle of development projects

Nice-to-have: 

  • Published research on signal processing NLP, NLU, or machine learning related to voice technology or messaging [preferred]
  • Experienced working in a cloud environment (AWS, Azure, GCP) or a containerized environment (Mesos, Kubernetes)  [preferred]

About the company: Flying High Airlines is focused on leveraging cutting-edge technology and data analysis methods to maintain our position as the preferred charter provider for our well-heeled clientele. Our data analysis and predictive analytics are the keys to having planes available at the right time and the right place for our clients. We need someone who is a driven knowledge leader who can work closely with our IT staff and operations teams, providing expertise in data analytics and transforming information into actionable business plans. If you want to leverage state-of-the-art tech in a high-end luxury business, then we want to talk to you!

Write an Effective Data Scientist Job Description

Crafting an effective and well-written job description is not always easy. A data scientist role is especially difficult because it is a highly technical role that encompasses a broad range of skills and varies greatly from company to company. The biggest challenge you will have is keeping it from being too lengthy and staying focused on the most important things your data scientist needs to know and what they will need to do. 

Condense your job description down to the vital information 

A data scientist must perform many complex tasks simultaneously. They are required to assess large amounts of data, quickly and efficiently identify essential data, and distill it down to the most valuable information. Likewise, your job description must be distilled to the essentials—nothing more. 

Ensuring your potential candidates will read the entire job description is a major objective you must keep in mind as you write. The most essential thing you need to do is keep your job description brief and concise. Listing all the pertinent job requirements for a data scientist role without creating a lengthy document can be difficult, but make a concerted effort to include the key info and “kill your darlings” by eliminating the unnecessary. 

Another major objective to remember is that you’re selling the job and your organization. You are trying to attract as many qualified applicants as possible. The best way to demonstrate what a job entails is by giving examples of issues a data scientist in your company will face. For instance,

  • Provide particulars of a problem they’ll need to fix (developing custom data models and algorithms to apply to data sets),
  • Discuss a gap they’ll fill (improvements to data quality and pricing model accuracy), or
  • State why you’re specifically hiring for the role (to institute a data science center of excellence). 

Returning full circle, George Mason University’s writing center stresses the importance of condensing your writing. According to the experts, writing concisely means that you “write what you mean—nothing more and nothing less." When you create a data scientist job description, consider each word choice and look for extra words you can remove without changing the meaning. Remove filler words and extraneous language.

Write to encourage diverse candidates 

Focus your requirements on the critical and unique needs of your company, eliminating common data science skills or traits, such as “strong data analysis skills.” This fluff clogs up your job description, not to mention that listing too many requirements tends to discourage diverse candidates from applying, especially in STEM. 

Writing a completely unbiased data scientist job description isn’t always easy despite your best efforts. It takes a conscious effort to use words and phrases that will encourage diverse populations to apply. MIT’s “Ensuring Bias-Free Job Postings” gives a high-level overview of the issue and has some pointers to help you avoid pitfalls that incidentally stereotype or alienate job seekers.

Edit and revise for content and grammar

The final and most important step to creating a great job description is editing and revising. Invite people in the data science function to review your work as any additional review is beneficial. Seek out constructive criticism, and be willing to make changes if appropriate.   

The worst thing you can do is submit a job description that has poor word choice, grammatical errors, or even worse, an inaccurate salary range with one too many zeros. Take your time, and when you’re done, you can be confident that you’ve published a high-quality listing that will attract the best talent for your data scientist role.

Start by framing your data scientist job description

We’ve shown you the examples and stressed the importance of writing your job description well, but we get that those things can seem a bit arbitrary when you’re staring at a blinking cursor on a blank screen. 

Check out the following outline that covers all the sections for any data scientist job description; then, all you need to do is fill it in, drawing upon inspiration from the examples and how-to guide above. 

Job details: This section is where you briefly introduce the company and the open position. You should leverage this section to immediately grab the reader’s attention. Offer candidates a taste of why your company is a great place to work. Then, in a couple of sentences, give them a quick blurb on the what and why of your data scientist role to encourage job seekers to read the rest of your job description.

What you’ll be doing: You may also call this section the “Roles” or  “Responsibilities” or “Requirements.” This section should be a bulleted list of the key tasks performed in the job role. Don’t make it too long. Ensure you include anything unique to or especially cool about the data scientist role in your company. Emphasize anything critical to the success of the position. As with any business writing, always use active verbs, avoid jargon and filler words, and be as specific as possible.

Examples:

  • Collect data from established channels and through means such as analyzing business results or the creation of new studies.
  • Create new, experimental frameworks to collect data, and build tools to automate data collection.
  • Carry out preprocessing (structured and unstructured data) cleansing, and validating the integrity of data to be used for analysis.
  • Mine and analyze data from company databases to drive optimization and improvement of product development, marketing techniques, and business strategies.
  • Correlate similar data to find actionable information and present results concisely.

Qualifications: This section is the make-or-break section. It quickly tells candidates whether they are a good fit for the job. Craft this section carefully. Always include anything critical and must-haves for the position. A good job description will list education, experience level, certifications, and any special requirements. 

Technical qualifications should always be described as specifically and objectively as possible. Consider whether you need to include soft skills at all. Perhaps you can discern that in an applicant’s data scientist resume or cover letter, saving room to list job-specific qualifications, such as text classification topic mining or speech enhancement.  

As you know, data science can include a lot of very technical requirements. Focus on the job's critical aspects, but avoid having too long a list. If your company has a lot of unique or extraordinary requirements, then use most of this section to cover that. As with every part of your job description, be brief, specific, and clear.

Examples:

  • Master’s or Ph.D. in statistics, mathematics, computer science, artificial intelligence, machine learning, or another quantitative field
  • 5+ years of professional experience as a data scientist or in a related role
  • 3+ years of work experience in voice-related projects
  • 3+ years of work experience in software development
  • Experience establishing supervised and unsupervised learning Client/NLP models, including data cleaning, data analytics, feature creation, model selection and ensemble methods, performance metrics, and visualization
  • Knowledgeable of signal processing techniques, including adaptive filtering, filter banks and wavelet processing, speech analysis, and synthesis, and speech and audio coding
  • Expertise in text classification topic mining, speech enhancement, and speech/audio coding and compression

Nice-to-have: If you have a lot of other skills that you are looking for, but they are not deal-breakers, then you may include these as a subsection to your qualifications. List all the things you consider to be key differentiators for candidates. Again, try not to list too many because this will discourage some candidates.

Benefits

The benefits section is a brief but standard item to include in your job description. Where you place it in the overall format is not so important, though it shouldn’t be the first item. If your company has outstanding benefits and/or something extraordinary to offer, such as an onsite gym or an unlimited vacation policy, then you may want to place this section earlier to snag the reader’s attention. 

About the company: The last section of your job description should contain additional information about your company. A data scientist will be interested in knowing a company’s strategy regarding data analysis, the usage of big data, and how the company leverages information technology. Cover these items, or anything directly related, in a sentence or two, noting the role of the data scientist.

On the job: the responsibilities of data scientists

Data scientists are researchers and masters of quantitative analysis. They investigate extremely large volumes of data and solve business challenges using data. Advanced data science involves identifying relevant data, detecting patterns and trends, and translating them into viable business plans and strategies. A data scientist must rapidly process and transform large data sets into understandable and usable business information.

Below are some sample roles a data science professional might fulfill. A single scientist may not fulfill all these roles, but this will give you are good list to choose from and add to based on your company’s specific needs.

Data Acquisition

  • The foundation of data science is investigating data. Having an inquisitive nature allows a data scientist to objectively review large databases and data sets from many sources, extract pertinent information, and solve an issue or answer a particular question.
    • Ask the right questions to drive the discovery process in the right direction.
    • Perform initial data investigation and exploratory data analysis.
    • Examine internal and external sources of data and detect important patterns and trends.
    • Determine processing methodologies to best transform data into usable business intelligence.
    • Think critically about how data is used, and assess and quantify strengths, weaknesses, and opportunities posed by data sources and processing.
    • Continually seek new data sources, ways to improve data processing, and new technologies to better utilize data.

Problem-solving

  • A data scientist defines how data can be used to make the organization successful. They provide expertise and guidance to the business on how to interpret information to formulate successful processes and business strategies.
    • Study business challenges, research relevant data processing techniques, and determine solutions through data analysis.
    • Apply data science techniques, such as machine learning, statistical modeling, and artificial intelligence.
    • Process and clean data to create useful databases and information.
    • Effectively integrate and store data into databases and business intelligence systems.
    • Identify problems in data acquisition, processing, and utilization; then, assess and resolve them.
    • Continually partner with business and data analysts to review data processing and make recommendations for improvements.

Communication and Reporting

  • A great data scientist must have excellent written and verbal communication skills. They are required to present and explain highly technical, complex information, and recommendations to all levels within the organization.
    • Partner with technical and data analysis teams to understand business processes and data sets to create documentation and presentations that translate data into coherent business information.
    • Present high-level results and recommendations to executive-level stakeholders.
    • Assess business feedback and make appropriate adjustments to data processing and analysis procedures.
    • Facilitate business communication and reporting for internal and external stakeholders.

Leadership

  • Data science is typically an upper and executive management level position. Management experience and strong leadership skills are non-negotiable. They should have a background in leading data analysis teams, have general management experience, and have proven abilities to collaborate with executives.
    • Provide direct expertise and leadership to business owners on the analysis and understanding of data utilization in business processes.
    • Consult with and provide direction to stakeholders (internal and external) regarding best practices and effective data use to improve business processes.
    • Lead teams, organizations, and programs to productively leverage data gathering and analysis to successfully complete tasks, initiatives, and projects.

Training

  • Data science is an ever-evolving field. Any good data scientist should have a strong interest in continued education and seek learning opportunities related to data science, statistics, and data analytics to improve their professional capabilities. They should not only be driven to improve their own knowledge but should also be strong proponents for organizational learning and training.
    • Research and stay abreast of the current state of data science.
    • Consistently spend time studying, completing professional training, and maintaining all certifications.
    • Engage with business units and regularly assess how they are utilizing data, information, and reporting tools to ensure that business strategies and objectives are achieved.
    • Spearhead ongoing training and technical education for data analysis professionals and business process owners to enhance and evolve the organization’s data analysis capabilities.