Expert: deep learning and AI · Ethics, privacy and careers · topic 60 of 62 · 30 min
Career paths: analyst, scientist, ML engineer, AI engineer
Explanation
Why understand the roles?
Job titles in data are used loosely, and the same title can mean different work in a bank, a startup and an IT services company. Knowing what each role usually involves helps you choose what to learn first, read job descriptions correctly and plan your next move. Many people start as analysts and move into science or engineering later.
| Role | Day-to-day work | Core skills | Typical tools |
|---|---|---|---|
| Data analyst | Answering business questions, reports, dashboards, ad-hoc analysis | SQL, Excel/Sheets, statistics, charts, communication | SQL, Excel, Power BI or Tableau, Python (pandas) |
| Data scientist | Modelling, experiments (A/B tests), forecasting, deeper analysis | Statistics, ML, Python, SQL, problem framing | pandas, scikit-learn, notebooks, experiment tools |
| ML engineer | Training at scale, deployment, pipelines, monitoring | Software engineering, ML, MLOps, cloud | Python, PyTorch, Docker, cloud ML platforms, MLflow |
| AI engineer | Building features on LLMs and pre-trained models: RAG, agents, evaluation | APIs, prompting, retrieval, evaluation, backend skills, safety | LLM APIs, vector databases, Python/TypeScript, eval tools |
Neighbouring roles include data engineer (building data pipelines and warehouses), analytics engineer (clean, tested data models for analysts), MLOps engineer and research scientist (new methods, usually needing a strong maths background and often a postgraduate degree).
Honest notes on pay in India
Salaries vary hugely by city, company type, skills and negotiation. As a rough, hedged picture: data analyst freshers often start somewhere around ₹3–8 LPA; data scientist and ML engineer roles for people with some experience commonly pay more, and product companies and well-funded startups tend to pay above service companies. Treat any number online, including this one, as a rough guide and check current postings and salary surveys for your city.
Entry routes
- From commerce, economics or any degree: start as a data analyst; SQL and Excel/BI skills plus one or two strong projects are often enough to get interviews.
- From computer science or engineering: data science or ML engineering, with solid Python and software skills.
- From software development: ML or AI engineering, adding ML fundamentals, evaluation and MLOps to your existing skills.
- From a domain (finance, healthcare, agriculture, marketing): domain knowledge plus data skills is a strong combination; employers value people who understand the business.
A suggested path through this course
Analyst track: modules 1–4 deeply, plus the SQL course on this site, then A/B testing from module 3. Data scientist track: modules 1–8 with many projects. ML engineer track: modules 5–9 and 11, with strong software skills. AI engineer track: modules 5, 6, 10 and 11, with backend development skills.
Common mistakes
- Collecting certificates instead of building and showing real work.
- Skipping SQL, which appears in almost every data interview.
- Applying only for "data scientist" titles and ignoring analyst roles that are a common way in.
- Chasing every new tool. Fundamentals change slowly; tools change fast.
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Career paths: analyst, scientist, ML engineer, AI engineer · 1/7
Data jobs share a base of Python, SQL and statistics, but their focus differs.
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In real life: From commerce graduate to data scientist
A B.Com graduate working in accounts learns SQL and Excel dashboards, automates a monthly reconciliation report and moves into a data analyst role at a fintech company. Over two years she learns Python, statistics and ML, runs pricing experiments with the product team, and is promoted to data scientist. Her finance background makes her models easier to trust and explain.
Practice (0/2 done)
Choose one of the four paths (analyst, scientist, ML engineer, AI engineer). Write a 12-week plan at about 1 hour a day: which modules or skills each block of weeks covers, two projects you will build, and how you will show them to employers.
Which role most typically owns deploying, scaling and monitoring models in production?
Quick quiz
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Related help
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Questions people ask about this
Open a question to see the answer and why. The first ones are this topic's own quiz, so try the quiz above before you peek.
Which skill appears in almost every data role's interviews?
Answer: SQL
Data lives in databases; SQL is the common language.
Who mainly builds RAG and LLM-based product features?
Answer: AI engineer
AI engineers build on pre-trained models and APIs.
What is a common and realistic way into data work for a non-engineering graduate?
Answer: Data analyst role with strong SQL, Excel/BI and projects
Analyst roles value business sense and SQL skills.
How should you treat salary figures you read online?
Answer: As rough guides that vary by city, company and skills
Check current postings and surveys for your situation.
Which role builds the pipelines that move and clean data into warehouses?
Answer: Data engineer
Data engineers build and maintain data pipelines.
Which skill most often moves a Linux admin towards DevOps roles?Related
Answer: Automation (Ansible, scripting) plus cloud and containers
Automation and cloud are the bridge.
Which is an honest salary range for freshers in cloud support or associate roles in India?Related
Answer: About ₹3.5–7 LPA, varying by city, company and skills
Fresher pay commonly falls in this range; product companies and some GCCs pay more, and it varies widely.
Which role is most focused on SLOs, on-call and reducing toil?Related
Answer: Site reliability engineer (SRE)
SREs own reliability: SLOs, incident response and automating repetitive operational work.
Many Go roles ask for 2+ years of backend experience. A good fresher strategy is...Related
Answer: Apply broadly for backend roles, show a Go project, and move to Go as you grow
Backend experience in any language transfers to Go.
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Next steps
- 2 more topics in “Ethics, privacy and careers” before its exam: Portfolio, projects and interviews, Capstone: an end-to-end project.
- Module 12 exam: Ethics, privacy and careers · 12 questions · 20 min, without help. 5 topics of this module still to finish first.
- 62 topics left in the course before the final exam.