Understanding the Different Roles in Data Science

. 5 min read

As you are on your journey to transition and upskill, the very first step should be to have a good understanding of what it is you are getting into in concrete terms.

For instance, which roles and what kind of work will you end up doing? Data Science is still a fairly new field and companies are still figuring out the right team structure and defining roles, so as to be able to make informed decisions using data. Designations and roles will vary in different companies, so the focus should be the responsibilities, as opposed to dwelling on the designation.

If you are looking to get a job in a large organization, you may be responsible for delivering only what your specific role demands. For instance, if you are an ML engineer you will be responsible for building models. However, working with a startup may give you the opportunity to do work on different roles at the same time and learn more.

Remember: No one knows everything! Even if you feel confident using any one skill, you are set to commence your job search journey.

Typical roles within a Data Science project

Let's review some of the typical roles involved in the end-to-end delivery of a Data Science project, along with their respective responsibilities.

Machine Learning Engineer

If you are already a software engineer, then transitioning to the role of an ML engineer is easiest. You already know the importance of writing quality code, code maintenance, and so on. You just have to carry forward these practices to your ML knowledge.

  • Key mandate
    • Create and deploy solutions using ML and DL algorithms to solve various problems.
  • Core responsibilities
    • Work closely with Data Scientists to transform what they wrote as a Jupyter Notebook or a Python script into a software that can be deployed.
    • Design and implement ML applications to address business challenges, benchmark infrastructure, and do A/B testing.
    • Work with product and engineering teams to improve data quality via tooling, optimization, and testing.
    • Monitor the model performance and finetune the model if required.
  • Mindset
    • Coding ninja with an application mindset towards problem-solving.
  • Skills
    • Python, Machine Learning, SQL/NoSQL, knowledge of AWS/GCP and REST APIs.

Data Analyst

If you are a fresher trying to break into a Data Science career, then a Data Analyst role is the ideal role to apply for. With a strong portfolio of data science projects and an ability to point out the right insights in the data, you will present yourself as the right candidate. Some companies advertise this position as a Jr. Data Scientist.

  • Key mandate
    • Acquire, process and summarize the different insights from data.
  • Core responsibilities
    • Scrape and query data while bringing it to a form that is suited for the stakeholders.
    • Manage the quality of data and acquire additional data if needed and augment to existing data.
    • Perform extensive EDA on the data and check the different hypothesis on data.
    • Interpret data properly and effectively communicate the findings through visualizations.
  • Mindset
    • Full-fledged data junkie with lightning fast ability to summarize insights in data.
  • Skills
    • Python/R, Excel, SQL, Visualization (Seaborn/Plotly), Statistics, Machine Learning.

Business Analyst

If you are a Masters in Business Administration or an equivalent business degree, then Business Analyst could be the right fit for you. Good domain expertise in fields like finance, retail etc, helps you break into companies solving the respective use cases. Having data science knowledge along with domain understanding helps you give the right perspective to the business.

  • Key mandate
    • Bridge gap between business and IT by providing technology-based solutions to enhance business processes.
  • Core responsibilities
    • Identify business needs and process data for easy analysis and understanding.
    • Use extensive domain knowledge to identify key gaps, challenges, and potential impacts of a solution or strategy.
    • Use storytelling and effective communication techniques to translate technical or statistical analysis into business intelligence.
    • Concentrate on retrospective and descriptive analysis of data and give business insights.
  • Mindset
    • Intimate business understanding with a sharp eye for data trends.
  • Skills
    • Business Administration degree, Domain Expertise, Data Visualization using a BI tool like Tableau, Storytelling, Data Modeling.

Data Scientist

  • Key mandate
    • Solve critical business problems using data to propose solutions for effective decision making.
  • Core responsibilities
    • Understand the business problems or market required capabilities that need a solution, and implement an analytics framework to solve it.
    • Acquire, clean, process and manage data from various sources and break the overall business problem into manageable chunks.
    • Create valuable and actionable insights from data by conducting a predictive and prescriptive analysis of data.
    • Enable data-driven decision-making by building models by communicating their findings to the business.
    • Solve multiple business-relevant questions at every stage of analysis and modelling.
  • Mindset
    • Data Wizard with a relentless drive to find answers in data.
  • Skills
    • Python/R, Data Wrangling, Machine Learning, Statistics, Data Visualization, Storytelling, SQL, AWS/GCP.

At the end of GreyAtom’s Data Science Masters Program, you will have the key skills required to be a Data Scientist.

Data Engineer

A Data Engineer is a data scientist’s and analyst’s best friend who keeps the data prepared for analysis as needed by both. A person with a data warehousing or database administration background is best suited for this role. If you are from a non-tech background then this role is not for you, and you must consider applying for data scientist/analyst role instead.

  • Key mandate
    • Design, build and manage the data infrastructure and ecosystem.
  • Core responsibilities
    • Develop, construct, test and maintain the data infrastructure of the organization.
    • Understand the various sources of data, collect, store and serve it in a format ready for analysis.
    • Responsible for conversion of unstructured data into a proper structured format, and integrate into existing pipelines.
    • Develop APIs to enable data consumption and improve existing systems by integrating newer technologies.
  • Mindset
    • Optimizer and Troubleshooter for all things data.
  • Skills
    • Basic Computer Engineering Degree, Distributed Systems, SQL, NoSQL, MongoDB, Python, Apache Spark, Data APIs, Data Warehousing.

Data Science Manager

If you are a senior person with lots of project management experience, along with data science knowledge, then this is the right role to aim for. Managing data science project is different from managing other types of projects, hence a senior person with hands-on data science experience would be a good fit for this role.

  • Key mandate
    • Manage data science and analyst teams and ensure the data science projects are aligned to organizational goals.
    • Execute and manage data science projects end-to-end, and ensure timely deliverables to the stakeholders.
    • Ask the right data science questions that need answering and ensure that the right expert is mapped to solve the right problem.
    • Plan and execute the data science roadmap for the organization and keep the leadership in the loop.
  • Core responsibilities
    • Align the team of data scientists to long term organizational goals and ensure that they are achieved.
  • Mindset
    • Data Science Cheerleader with a long term vision and goals for team.
  • Skills
    • Database Systems, Machine Learning, Leadership and Project Management, Communication and Storytelling.

Food for thought

  1. Look at the roles and responsibilities instead of the job titles before applying. Even if you have some key skills, not all, don’t feel overwhelmed; go ahead and apply. JDs are typically a myth in Data Science.
  2. Know that since Data Science is an emerging field, there is not much clarity regarding the roles. Seek more clarity on the role during your interview process. Ask specific questions like: “Explain to me a day in life scenario for this role”
  3. Look out for red flags in the job description. If someone is asking 6+ years of experience for a 5-year old field, then that is definitely not the right company.

Now that you have some clarity about the different data science-related roles, in further concepts, you will understand the journey from your current profile into data science as well as approaches to succeed.

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