Tag: machine learning

A LinkedIn post by Eric Weber on career paths originating from Data Science started me thinking along the same lines. Through this blog, it is my humble intention to curate the different changing roles that have evolved in Data Science, as well as speculate on how these new roles came into being. 

When I first started hearing about Data Science, way back in the early 2000s, some of the earliest roles were Data analysts. Then, it seemed like an outgrowth from the Data entry roles that came into existence in the late 20th century. Then in 2011, McKinsey came out with its paper “Big Data: The next frontier for Innovation, competition, and Productivity” which brought to the forefront the inability of many organizations to harness the power of data because of a lack of skilled personnel. Visharg Shah’s article on Medium explains how the field of data science grew rapidly. the reasons can be summarized as due to these reasons – 

• Exponential growth in data collected from various sources by companies
• Massive growth in processing power and processing capacity toward the analysis process
• Availability of tools for analysis and open access to frameworks and modules simplifying the task
• Organizations find value in analyzing historical data and big data collected
• Increased importance in using data for decision-making augmenting traditional methods
• Democratization of data science leading to increasing adoption by most industry segments
• Digitization of historic data especially those held by public agencies 

Even as the companies scrambled to hire the best data science talent, they conceded that the job market sorely lacked the talent for these roles. 

Following that by the mid-2015s, the roles had evolved into the roles of Data Scientist, Data Analyst, Data Engineer, and Business Analyst. These roles were segmented within the field of Data science depending on the skills needed and the role within the organization.

As companies have started to dig deeper into their data, they realized that a person with just knowledge of data science might not be enough. Organizations expected more from their hires, seeking those – Who knew what type of data they were dealing with, who had domain expertise in the industry, what insights could be developed, how to visualize these analytics, predict trends in the industry, etc. New roles came into existence like Linguistics Analyst (Uses knowledge of different cultures to provide an accurate picture for decision-makers including dialect, nuances, body language, and cultural context) or Sustainable Chemistry Analyst (Performs product testing, maintaining a database of sustainable chemicals with extensive operational responsibilities). Thus, we can see that at a basic level, the roles have not changed much, and yet they have.

Let us consider an entry-level Procurement Operations Analyst role that I found from Amazon to make my point.

Main Responsibilities

• In partnership with the Procurement Operations Manager, provide procurement operations support for the fulfillment center, including forecasting of non-inventory products, inventory management, non-inventory flow and space models, cycle counts, supplier management, procurement transaction, and expediting support
• Led team of non-inventory receivers to ensure the building has adequate resources and is set up for success
• Develop deep knowledge of non-inventory items and align with like buildings to drive best practices
• Manage KPI to measure, control, and benchmark procurement processes including the creation of recurring metrics reports driving improvements for the Operations network
• Develop relationships across the building and network to ensure best practices are being shared and implemented
• Align with internal customers, Finance, and Procurement Operations to understand budgetary targets by building and developing methods of measuring and defining savings, value, and other category metrics
• Using input from the category team, build the category metrics model to track and monitor the performance of the category strategy
• Measure actual vs planned savings; as savings trends are identified, own action plans to meet goals and develop solutions
• Work in partnership both internally and with suppliers to develop innovative solutions to provide Procurement support to the Operations network
• Develop and implement ways to measure suppliers to drive continuous performance improvement on behalf of XXXXXXXX
• Coordinate the demand identification, procurement, and inventory management of all non-merchandise items required for building operations. This includes corrugate, packing materials, labor, janitorial services, etc.
• Partner with the Category team to manage and maintain supplier scorecards
• Partner with AP, Suppliers, and various internal teams to ensure the timely resolution of vendor payment issues
• Support the procurement operations and category management teams
• Work is done in a warehouse environment that requires frequent walking around the building. You should feel comfortable working in an environment with varying temperatures as many buildings have dock doors that open throughout shifts.

Basic Qualifications

• Completed Bachelor’s Degree in Supply Chain Management, Business Administration, Engineering, IT, or related field, OR 2+ years of XXXXXXXX experience
• 3+ years of experience in supply chain operations
• 1+ years’ experience using Microsoft Office, particularly Excel and analytical platforms, including but not limited to the ability to analyze data using pivots and V-Lookups
• 1+ years of people management experience
• Experience understanding process flows and suggest improvements to deliver cost savings, inventory reduction, or other benefits to the site.
• Supplier/ vendor relationship management experience

Preferred Qualifications

• Procurement experience preferred
• Experience in Coupa or other financial management/procurement software
• Experience with cost accounting
• Lean / Six-Sigma knowledge
• Must be highly self-motivated and customer-centric
• Ability to work with ambiguity
• Provide a positive customer experience internally and externally

If we look at the highlighted content, we can see clearly how Amazon seeks to utilize data science for procurement roles. Understanding data points, KPIs and other industry thought processes and finally data-driven decision-making become important along with a need to have domain expertise. For someone beginning to learn data sciences, it is essential to find application areas and actively work on projects to remain relevant in the job market. I believe that future trends would lead to even more specialization based on software/applications as well as industry segments thus resulting in more roles. 

For now, I aggregated data points on various roles based on the input of LinkedIn users who commented on the LinkedIn post I mentioned earlier. Various roles that I was able to aggregate are:

1. Analytics engineer
2. Analytics manager
3. Analytics Translator
4. Applied scientist
5. BI Consultant
6. Big Data Developer/Architect
7. Biostatistician
8. Business intelligence engineer
9. Category Manager
10. Chief Data Officer
11. Chief Privacy Officer
12. Continuous Improvement Managers
13. Data Architect 
14. Data Domain Leader
15. Data Engineer
16. Data Ethicist
17. Data Governance Analyst 
18. Data Governance Lead 
19. Data Maintenance Specialist
20. Data Management Lead 
21. Data Miner 
22. Data Modeller 
23. Data Platform Engineers 
24. Data Quality expert
25. Data steward
26. Data visualization engineer
27. Database Developer
28. DataOps Engineers 
29. Decision scientist
30. Director – Data Governance
31. Econometrician
32. Enterprise Architect
33. Enterprise Data Architect
34. ETL Developer
35. Financial Analyst
36. Insights specialist
37. Learning Analytics 
38. Logistics Manager
39. Machine learning engineer
40. Market Research Analyst 
41. Marketing Scientist
42. Metrics analyst
43. Ontologist (Semantics expert)
44. Operations Analyst
45. Operations Manager
46. Performance improvement leader
47. Pricing Manager
48. Product analyst
49. Product Manager
50. Product Specialist
51. Psychometrician
52. Research scientist
53. Sales and marketing analyst
54. Scientist Educational 
55. Citizen Data Scientist

This list of job roles is not comprehensive and many more roles are emerging every day. I want your help in my attempt to compile an exhaustive list of job roles involving data science. I want to create this list to identify domain expertise and data science skills that will help guide data science enthusiasts in the right direction. Do you know of any roles where data science or data analysis is recently become essential? Did we miss out on any roles? Let us know your thoughts.

The post was written by Yashwant Kuram