Data science careers: salaries, skills, positions, and employers

TABLE OF CONTENTS

1. Choosing your Role
2. Data Science Positions
3. International Jobs and Salary
4. Data Science Companies 
5. How to Apply
6. How to build your resume for a Data science career
7. How to get interview calls
8. Interview Preparation
9. Skill sets
10. Source

1. Choosing your Role

3 Buckets in Data Science

• Analysis: Business Analyst, Product Analyst, Risk Analyst. Here you study the data to create insights. Overall Skill sets required SQL, Tableau, Microsoft Power BI, Excell, and Some amount of Python.  The number of Jobs is more, the Salary is less than the other 2 and moreover, it depends on the company.
• Building Models & Making Algorithms: ML Engineering, DL Engineering: here you build everything from scratch. Overall Skill sets required Math, Python, Industry Expertise, and Intuition Instinct. The number of jobs is less compared to the other 2 but the salary is more compared to the other 2.
• Server Infra, helping above both people: MLOps, HDFS. Here you set up and configure the process. The number of jobs and salary are between these 2 segments. Overall Skill sets required AWS/AZURE/GCP, Database, HDFS, DFS, DBMS.

So below are the few positions on basis of the above 3 buckets.

2. Data Science Positions: 

The following positions in the field of data science are posted by different companies for data science requirements on their career page. The Name of the particular positions varies from company to company.

• Data scientist
• Lead Data Scientist
• Staff Data Scientist
• Associate Data Scientist
• Senior Data Scientist
• Junior Data Scientist
• Data Scientist ML
• Associate Data Scientist – ML & NLP
• Executive Director/ Director – Product Analytics and Data Science
• Team Lead – Data Science
• AGM-Data Scientist
• Senior Director, Head of Data Science
• Manager-Data Science
• Head Data Scientist
• Principal Data Scientist
• Associate Director, Data Science
• Applied Data Scientist
• Data Engineer
• Big Data Developer
• Sr Data Analyst
• Big Data Engineer
• Senior Associate Data Engineering
• Chief Expert – AI and Data Analytics
• Cloud Data Engineer
• Business Analytics- Specialist
• Security Data Privacy Security Architect
• Data Engineer I- Support Engineer
• Senior Consultant: Data Engineer
• Data Specialist
• Senior Data Engineer
• Data Engineering Lead
• Partner Customer Engineer, Data Management, Tech ISV Partners
• Data Engineer (Big Data, Hadoop)-Lead
• Lead – Software Engineering – Data Engineering
• Senior Data Platform Engineer
• Global Data Management – Quantitative Data Scientist
• Data Analyst
• Weather and Climate Data Scientist
• Data Engineer, AVP
• Data Quality Associate
• Data Annotator
• Blockchain Data Labeller
• DevOps Engineer
• Manager – Data Science
• Machine Learning /Data Engineer
• Data Modeling Techniques and Methodologies Application Lead
• Senior Data Processing Analyst
• Data Engineer Sr Developer
• Azure Data Engineer
• Data Engineer – II (Level 5)
• AI ML Engineer
• C3 Senior ML Engineer
• Team Lead – Backend & AI / ML
• Assistant Manager – AI/ML
• Senior AI/ML engineer
• Sr Snowflake Data Engineer
• ML Engineer
• BizOps Engineer II
• Lead ML Platform Engineer
• AI/ML engineer
• ML Research Engineer
• Chief Expert – AI and Data Analytics
• Computer Vision and ML Researcher
• ML Architect
• Machine Learning Engineer
• ML Ops Engineer
• Engineering Manager for ML product
• AI/ ML Lead
• ML QA Engineer
• AI Ops Engineer
• Senior Machine Learning Engineer
• Lead Engineer, Machine Learning Engineering
• Engineering Director, Editors Intelligence
• Data Engineer – Predictive Maintenance
• Engineering Services Engineer ML
• Senior Engineer: AiOps Platform
• Principal AI/ML Architect
• Researcher – Deep Learning & AI
• Sr. Analyst( ML, AI & DL)

3. International Jobs and Salary

What is the Pay by Experience Level for Data Scientists?

4. Data Science Companies  in terms of different Industry

5. How to Apply

Specific Vs Wide Approach

Specific

Everyone has a particular dream company, they wanted to work with them.  So if you are one of these people, research the particular company you want to join like the skillset they asking for, location, and salaries, you can find everything on their career page and LinkedIn. So just explore.

Wide Approach

If you are new to the Data Science Industry, currently you are learning and you don’t know where to go then this is for you.

Step 01: Visit LinkedIn.

Step 02: Go to Jobs

Step 03: Search the different roles in the data science profile, above I have listed the positions.

Step 04: Explore the skill set they need and compare your skills, if it is matching then pick the particular position and search for that same position in a different company.

Step 05: Use the filter in the LinkedIn search box to get your preferred result.

Step 06: Make everything needed and apply. You can choose easy Apply on LinkedIn.

NOTE:
1. Before appearing for an interview with Big companies, you must experience the interview process with small companies. It is suggested because you will rectify yourself in every layer.

2. Few company positions are not available on LinkedIn you can find these on their career page and apply there.

6. How to build your resume for a Data science career: 

The below video will help you to build your resume perfectly.

Some additional tips and tricks.

7. How to get interview calls

1. Resume Keywords: Make sure to add keywords related to your industry in your profile and resume. e.g: AWS, AZURE, GCP, HDFS for cloud Engineer positions. Tailor your resume and cover letter to the specific job you are applying for.
2. Networking: Send requests to a different person related to your expected positions and industry. Connect with HR of different companies.
3. Reference / Recommendation: If you know someone from your expected company then share your resume with them and ask them to refer you for the particular positions as many job openings are not publicly advertised.
4. Approaching With Problem Statement: If you are thinking you have solutions to the problems of the expected company, just approach them with your solutions.
5. Use online job search platforms and professional networking sites to find and apply for relevant job openings.
6. Keep your online presence professional, as many companies will check your social media profiles as part of the hiring process.
7. Be proactive, reach out to companies that you are interested in working for, even if they don’t have any job postings
8. Make sure your online profile on professional networking sites like LinkedIn is up to date and complete
9. Follow up with the companies you’ve applied to about the status of your application.

Here Is the list of a few platforms to search jobs:

• Linkedin
• Google Jobs
• GitHub jobs
• Stack Overflow Jobs
• Apna
• Monster
• Indeed
• Glassdoor
• Facebook job
• Upwork
• Fiverr
• Freelancer
• Dice
• Hired
• AngelList
• TechCareers

8. Interview Preparation : 

Different rounds of interviews:

R1- R2: DSA / SQL

R3-R4: Data science related: ML(Theory, Code)

R5: HR

The Following videos will help you prepare interviews for with big Companies.

1. Data science includes software engineering.
   1. DSA
   2. LLD
   3. Speed
   4. Optimization
2. Mathematics
   1. Probability
   2. Statistics
   3. Linear Algebra
   4. Coordinate geometry
   5. Calculus
3. ML/DL:
   1. Image processing and Prediction
   2. Text Processing and Prediction
   3. Video Processing and Prediction
   4. Audio processing and Prediction
4. MLOps, Cloud, ML system design:
   1. Data Pipeline
   2. ETL
   3. AWS
   4. GCP
   5. Azure
5. Big Data and DFS:
   1. Kafka
   2. Airflow
   3. pyspark
   4. HDFS
6. Tools:
   1. Linux
   2. Docker
   3. Git
7. Web:
   1. API
   2. Scraping
   3. Automation
   4. Development
8. Database:
   1. SQL/MongoDB
9. Visualization
   1. Tableau
   2. Microsoft PowerBI

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