In God we trust, all others must bring data — WE Deming
Startups are hot, data science is equally hip, making analytics/data job at a startup one of the most sort after jobs.
The job can vary in responsibilities and needs from startup to startup and the kind of people it needs. Given this context, it’s important for either party to find the proper fit by better understanding each other’s needs. The idea here is to better understand the space and the type of roles.
The following criteria were applied to come up with a sample of jobs :
- Source: Angellist
- Region: NCR (Delhi, India)
- Activity: Active in last 15 days
- Job: Full Time
Data Jobs Attributes
- Sector: The kind of domain they are in SAAS, FinTech etc.
- Type: B2B or B2C. Each one of them has their own peculiarities in terms of roles.
- Job Title: There are broadly three roles Data Analyst, Data Scientist, ML Engineer.
- Seniority: Entry or for experienced folks (generally >2 years)
- Salary Range: Got three buckets here: low, variable, high.
- Employees: Got three buckets here too : (0-10), (10-50), (50+)
Why these categorisations?
- Domains or the sector you work becomes important in the long run. It is necessary to specialise either in a specific domain or specific type of problems e.g.: You can be an expert in financial data sets or solving marketplace auctions problems.
- B2B sometimes has client presentations and similar external stakeholders which demands greater interpersonal skills. B2C deals more with internal stakeholders, so you need not be that proficient.
- The three roles have very different sets of responsibilities and expectations. It’s important to understand this distinction and choose the one, which fits the candidate best. There are no great or bad jobs, good or bad fit.
- Whether it requires experience or not.
- Pay has three bands: low, variable and high ranges.
- An employee has three bands: few (0-10), medium (10-50), high (50+). The number of employees can act as a proxy for the startup’s maturity stage. Pre product-market fit, growth or mature phase.
- B2B space is dominated by SAAS firms, a lot of the AI/ML platforms with plug and play solutions. Also, B2B has a nice mix in terms of the job titles with plenty of entry-level jobs.
- B2C has a greater mix of domains and also a greater proportion of Data Scientist roles. This could be because of the need to understand customer behaviour and modelling. Has both entry and experienced roles.
- The size of the firm has a very clear impact on the salary bands. Larger firms are very clear in terms of pay bands. The ones in the growth phase or just post-product-market fit are looking for people in both entry and experienced roles thereby offering a range of salaries.
Understanding Job Title
- Data Analyst: Business heavy role with SQL, web analytics, visualisations forming the bulk of the job responsibility. Would likely belong to the product group.
- Data Scientist: The modelling job with expertise in solving a specific type of problems: survival analysis, auction modelling, operations research, optimisation. Mature firms have a separate division from product and engineering.
- ML Engineer: Implements the model into production, more code and less math than the data scientist role. Dockers, containers & ensuring a smooth ML system, becoming a part of the engineering team.
The idea behind the above analysis was to better understand what kind of data role, one is picking up and how they should intend to go about it. Getting the fit is of paramount importance because what might be a bad job for you might be great for others. The end goal is to take better decisions and build better systems. You can contribute in either way.
Belong team’s template was an inspiration for creating this approach. They have quite an interesting approach to getting data science jobs.