I ought to have broken this down even further, because this is a pretty hefty topic on its own. We’ll talk about domain knowledge, job types, and the variation of responsibilities between roles. I will also take some time to go over typical requirements of each– this is something we sort of covered in universal skills but there’s a distinct difference there.

I have so far observed three specific paths for data:

Data Analyst

Data Scientist

Data Engineer

I have an exhaustive list of job titles in data that go across many different industries and domain knowledge, but the main difference between them is their core responsibility.

Analyst – Analyzes the data in the specific department or field (think marketing, or financial portfolios). Think of this as “the now,” or “then”: “This is what the data is telling us.”

Scientist – Applies statistics and machine learning concepts and principles to create models and determine outcomes. Think of this in terms of predictive analysis: “This is what we can do to alleviate x problem in the company.”

Engineer – Back end, working with SQL, Python (NLP most often), Tableau Prep Builder, Cloud software. This type of role is focused on streamlining what’s called the ETL Process, or the Extract, Transform, Load process (the words can shift depending on your methods).

Like I said, these tend to be distinct roles, but sometimes they can get muddled, or a company will want you to do a wide breadth of responsibilities. The opposite can also happen, especially with an engineering role, where they want you to focus on developing SQL, or with analysis, where they want you to focus on developing dashboards and visualizations in Tableau.

A good chunk of why I got into data analysis to begin with is because of my domain knowledge. I wanted to use that to help communities that were underserved or had little data. This also happens to be why people who transition into data tend to be strong candidates as analysts: they know the industry, they have that knowledge and understanding of how it works and what to look for and what certain things mean.

Domain knowledge is probably something I should add to the jargon list. It essentially means previous knowledge you have in a specific industry or department. HR Analysts can be people who were HR associates in the past, Financial Analysts could have been accountants, a Data Manager at a non-profit animal shelter could have previous vet tech or animal handling experience.

What I’m saying here is that you should feel comfortable and confident in your transition because you have knowledge from where you came from. You have a specialty. And, if you don’t want that specialty and want to move into something else, we’ll cover projects in this series too that should help.

Below is a list that was compiled by the Women in Data (link to the organization here) founder, Sadie St. Lawrence, on the Women in Data slack channel. It is an *exhaustive* list of all of the different roles and job titles that occur in data. A lot of these can depend on domain knowledge, department, job responsibilities, educational background, interest, and growth within a company.

Job Titles in Data

1. Data Scientist
2. Data Analyst
3. Data Engineer
4. Data Journalist
5. Data Storyteller
6. Data Architect
7. Database Administrator
8. Data Visualization Engineer
9. Data Product Manager
10. Data Evangelist/Advocate
11. Data Governance Analyst
12. Data Governance Manager
13. ML Engineer
14. ML Scientist
15. ML Ops Engineer
16. AI Engineer
17. Deep Learning Engineer
18. Cloud Architect
19. Solutions Engineer
20. Computer Vision Engineer
21. Autonomous Driving Engineer
22. AI Product Manager
23. AI Research Scientist
24. Applied AI Scientist
25. Applied ML Scientist
26. Applied AI Scientist
27. Analytics Engineer
28. Analytics Consultant
29. Analytics Translator
30. Analytics Manager
31. Sales and Marketing Analyst
32. Market Research Analyst
33. Business Analyst
34. Metrics Analyst
35. Customer Support Analyst
36. Reporting Analyst
37. Supply Chain Analyst
38. Customer Retention Analyst
39. Logistics Analyst
40. People Analyst
41. Production Analyst
42. Finance Analyst
43. Operations Analyst
44. Fiscal Analyst
45. Budget Analyst
46. Crime Analyst
47. Ops Tech Engineer
48. Insights Specialist
49. Product Data Scientist
50. Product Analyst
51. Product Manager
52. Business Intelligence Analyst
53. Business Intelligence Developer
54. Business Intelligence Engineer
55. Operations Research Analyst
56. Qualitative Analyst
57. Decision Scientist
58. Research Scientist
59. Applied Scientist
60. Statistician
61. Data Solutions Analyst
62. Computational Scientist
63. Cancer Data Scientist
64. Health Data Scientist
65. NLP Scientist
66. Research Scientist – NLP
67. Motion Health Data Scientist
68. Clinical Data Analyst/Scientist
69. Public Health Data Analyst
70. Healthcare Business/Data Analyst
71. Data Analytics- Clinical/Biomedical
72. Quant Modeler-Data Scientist
73. Predictive Modeler
74. Marketing Data Scientist
75. Data Science Coach
76. Data Science Instructor
77. Data Quality Specialist
78. Epidemiologist
79. GIS Analyst
80. Blockchain Developer
81. Blockchain Architect
82. Enterprise Architect
83. Transportation Planning Specialist
84. Performance Metrics Analyst
85. Data Program Manager
86. Data Acquisition Specialist
87. AI Analyst

A big thank you to Sadie for letting me share this list. This was a joint effort that a lot of members helped on, and it started out at half the size it is now.

A lot of the reason for sharing that exhaustive list and for getting deeper into the roles for data careers is to give you a point for you to reach for. If you see something here you like, research it! Look into some job descriptions for it, see if it’s something you’d be interested in doing in the long run, find a community of people who are doing what sounds interesting and see what their jobs are like.

I hope that I’ve done justice for each of the careers, even if I didn’t go into depth for each title. Some of these roles will be more public facing (notably those in government positions have the capacity for that), and some will just be working within an internal team.

The point overall is, there are *so* many options, and you would be doing yourself a disservice not to look into them. You may fall into one of them on accident, you may not know you love machine learning and AI until you get into it, or you may just like communicating business problems with visualizations.

The next post will cover education and learning, certifications, programs you can join, and more to help you in your transition.

Whether you’ve just joined for this post, of you’ve seen all of them so far, thank you for reading!