Active Hackathon

Entry-Level Jobs For Those Starting A Career In Data Science

Data science

There is enough literature to affirm that learning data science skills can put you on a path to a lucrative career. The profound growth in the field has seen a lot of people switching careers and making a professional transition to data science. Unfortunately, despite employment opportunities soaring, you may not necessarily land a job as soon as you acquire the required skills, or graduate from a formal education program.

Since there is no universal definition of a data scientist that every company agrees on, positions that sound the same may actually come with disparate skill requirements across firms. These roles may also be misleading in that sense and not align with your long-term career goals.


Sign up for your weekly dose of what's up in emerging technology.

To offset this, it will be a good idea to probe deeper and internally visualise what your career in data science should look like first. Explore the question — where can my new skills take me professionally? — and once you have an answer to that, select a role by its description and one that lines up with your goals and interests.

There are three broad areas in data science — data analyst, data scientist, and data engineer. 

While data analysts are mainly involved in gathering data, organising databases, analysing through exploratory and statistical means, and visualising to find patterns accordingly, data scientists draw insights from data with a key focus on neural networks and other machine learning techniques. They also communicate these insights to stakeholders by employing various storytelling techniques.

Data engineers, on the other hand, manage a company’s data infrastructure and create pipelines for data scientists to streamline their analysis processes. This involves working with a lot of teams within organisations to create data collection strategy as well as with data science teams for simplifying analytics workflows.

Of these, the job of a data analyst is typically considered the most basic in the field of data science. However, even here, responsibilities may vary, as would salary ranges.

Although we cannot cover every potential job title and description that you may encounter in your job hunt, we have filtered down some of the major roles in the data science ecosystem that could be relevant for you as you look to make a career switch. 

Data Analyst

A data analyst encompasses a wide variety of roles, but your primary job would be to access

data, clean it, extract it, analyse how it aligns with key business questions, and develop models that can form the basis of the company’s strategy. As you climb up the ladder, it will entail working closely with various departments and executives in the organisation.

Since this role is open-ended, you can eventually shift towards the role of a data scientist by focusing on machine learning and continuing to build your data science skills. Alternatively, if your interests are aligned more towards working with cloud (both private and public), DevOps tools such as Docker and Kubernetes, and employing programming languages like Python, Java, Scala, etc., you can gradually work towards a data engineering role.

Data Scientist

The general progression of a data analyst is to work towards the role of a data scientist. In fact, oftentimes the primary responsibility in both positions will largely be the same. But data scientists additionally have to build huge ML models and analyse past data in order to make accurate predictions about the future. You need to have a knack for problem-solving using creative means to do well in this role. This also makes this role very interesting as they are allowed the flexibility to pursue their own ideas in order to locate unique trends in data.

Business Intelligence Analyst

The primary job of a BI analyst is to scour historical data of a company to analyse and report market and business trends. This includes quantifying observations and calculating KPIs – tasks for which they need to be familiar with software-based tools, as well as programming languages like Python or R.

Data Architect

Data architects essentially create the database of a company from the ground up. They primarily design the way it will be used for a wide range of businesses and solutions. Although this is not core data science, it still involves a lot of work with data, including its design and the way it is processed. They also often collaborate with data scientists in the company to jointly work on common business goals.

Quantitative Analyst

To work as a quantitative analyst, you need to have an in-depth knowledge of statistical techniques. This is because they are expected to use advanced statistical analyses to make predictions related to matters of finance. Here, you have a good opportunity to use the data science programming skills you acquired over time. You are also expected to have a good understanding of how to apply ML models to undertake the quantitative analysis.

More Great AIM Stories

Anu Thomas
Anu is a writer who stews in existential angst and actively seeks what’s broken. Lover of avant-garde films and BoJack Horseman fan theories, she has previously worked for Economic Times. Contact:

Our Upcoming Events

Conference, Virtual
Genpact Analytics Career Day
3rd Sep

Conference, in-person (Bangalore)
Cypher 2022
21-23rd Sep

Conference, in-person (Bangalore)
Machine Learning Developers Summit (MLDS) 2023
19-20th Jan, 2023

Conference, in-person (Bangalore)
Data Engineering Summit (DES) 2023
21st Apr, 2023

Conference, in-person (Bangalore)
MachineCon 2023
23rd Jun, 2023

3 Ways to Join our Community

Discord Server

Stay Connected with a larger ecosystem of data science and ML Professionals

Telegram Channel

Discover special offers, top stories, upcoming events, and more.

Subscribe to our newsletter

Get the latest updates from AIM

Council Post: How to Evolve with Changing Workforce

The demand for digital roles is growing rapidly, and scouting for talent is becoming more and more difficult. If organisations do not change their ways to adapt and alter their strategy, it could have a significant business impact.

All Tech Giants: On your Mark, Get Set – Slow!

In September 2021, the FTC published a report on M&As of five top companies in the US that have escaped the antitrust laws. These were Alphabet/Google, Amazon, Apple, Facebook, and Microsoft.

The Digital Transformation Journey of Vedanta

In the current digital ecosystem, the evolving technologies can be seen both as an opportunity to gain new insights as well as a disruption by others, says Vineet Jaiswal, chief digital and technology officer at Vedanta Resources Limited

BlenderBot — Public, Yet Not Too Public

As a footnote, Meta cites access will be granted to academic researchers and people affiliated to government organisations, civil society groups, academia and global industry research labs.