Data-Driven Career Coaching

7 min readFeb 17, 2022

What is Career Coaching ?

Career coaching is needed for every candidate at various stages of his/her professional journey. Career coaches could be people from your friends circle, family or recruiters that may have helped you find/switch a job. You are lucky if you already have a career-coach that is part of your trusted circle and your association/relationships has been for years to decades. Otherwise finding a career-coach typically starts when you are job-hunting and recruiters/staffers/senior colleagues/academically achieved are some of the professionally and qualified to play the role of a coach.

Who can be a career coach ?

Generally speaking an ideal career coach is someone who has been through the journey (has walked the path) and hence is in a position to advise. However learning is a life-long journey and being regarded as knowledgeable patron and being respected is “in the eyes of beholder” and subjective. However objectively, some things like education, achievements, past leadership positions, brands/companies associated with, and no. of years of coaching experience are some other attributes that make someone a career-coach.

Further selection of a coach also depends on

Industry/Domain expertise
Geo (physical proximity as some conversations are better face-face)
Prior experience of the journey (relevant to the candidate)
Leadership and Management experience — both tech and people wise
Achievements etc.

Candidates/Job-Seekers

Depending on years of experience, skill level, startup/corporate experience, geo location, aspirations and so on — candidates/job-seekers can be segmented in umpteen ways.

A candidate’s career journey is largely decided by his/her personality and below segment-attributes can cross-over into each other. For e.g. a 1–5 years experienced patron can have the maturity/skills of a typical 10+ years experienced. Below segments are one way to segment

🧑‍🎓 Early Bird

Fresh Graduates (some fresh graduates already have enough work experience working part time. We can make exceptions after talking to them)
Studying but want a part-time job
New to Industry (candidate is switching to a completely new industry but carries transferable skills)
Not very clear on priorities or able to determine value in the market

🏄 Take-off Ready

1–5 years experienced in a professional job
Worked with a startup/SME/enterprise — but has been working continuously at least for 1–4 years and hungry to learn as much as possible
Can be identified with an industry and job-family
Starting to understand priorities, but still honing themselves — for e.g. I need more money, or I need more responsibility etc.

👨‍💼 Cruising

5–15 years of experience
Has been through various phases of working in a job — been through ups and downs and shaped with opinions and skills
Sometimes this segment is also the one that settles down in a geo, married and has family, and-or moved from home country to another country (e.g. H1-B for US, High Skilled for UK/Auz etc.)
Generally understands priorities (exceptions apart) but at the same time in a convergence mode

👩‍⚖️ Settled

15+ years of experience
Generally speaking — either already prioritized what he/she wants out of professional life OR directional acceptance of life opportunities
Can further be categorized into individual-contributor (senior / principal or distinguished professionals) or management-chain (people management roles)
More clearly understands priorities and starts to evaluate opportunity-cost for every new opportunity

What is Data-driven coaching ?

The engagement with a coach begins with discovery of the candidate’s profile, motivations, expectations, capabilities, skills and so on. Subsequently, standard frameworks of discussions (e.g. S.M.A.R.T goals, S.T.A.R articulation etc.) drive the conversations and action items, execution strategies, pivoting and continuous feedback-loops are regular steps in the process. While high-level process remains the same, our research shows that candidates/job seekers associate higher value with specifics and how the engagement helped them meet expectations. In other words, they expect clear measurements and persuading/convincing recommendations that are backed by data.

ROI (return on investment) is an intuitive expectation for all humans whether it is explicitly said or not. Hence coaching conversations are more valuable when structured, objective and data-driven (over subjective discussions)

Subjective and philosophical connotations during the conversations are still relevant— because there are so many mysteries in the world that data / science / human rules cannot answer (yet). But one of our goals is to get the two parties (aka. coach and the candidate) be on the same page as much as possible and hence data-driven content is quintessential

Use Case / Scenario

John has been an engineer, entrepreneur ,management professional and product guy for at least 25 years. John wanted a change in career and he was approached by a firm in Georgia for a Sr. Director of Analytics position in Atlanta, Georgia. After multiple discussions, he was at a point to know if the base salary ($150k-$180k) is fair . Lets see how a coach used https://symmetrics.fyi to get the answer

Get the Data: All salaries with permutations and combinations of titles with the words [Senior/Sr/Analytics/Business Intelligence/Insights/Reports/Datawarehouse] were searched on Symmetrics for all years 2001–2021. Sample data as below

Time freshness: There were 1000’s of records and comparing a salary 5 years before is not so relevant (inflation adjustment and just because of old data). So narrowed data only for past 4 years. The average was $150k and median was $155k
Pandemic Effect: It was important to have data before and after pandemic (because pandemic did increase salaries for every job, but we wanted to know by what margin, hence pre-pandemic is required). Before pandemic i.e. for years 2018,2019 the average, median was $120k,$122k . During pandemic i.e. for years 2020,2021 the average, median is $190k, $160k
City and State: Pre-pandemic salaries were heavily dependent on Geo , however since this was a Software/IT job and during pandemic many companies opened up for remote working — it does still have an effect but much less. In this case, we filtered out CA, WA, NY, TX (the highest paying states generally for Software) because the offer was from a company that had its roots in GA only. The average, median for GA for 4 years was $130k, $120k. But during pandemic this shot up to $160k, $155k
Industry : It might not be right to compare the salary between a company that is in oil & Gas industry to the one in High-Tech industry (one can argue we can, but we leave that to the individual if he/she is able to convince), so we included employers from the same/similar industry. The average, median for employers across all industries for the years 2018–2021 was $130k, $125k

So to map it to the requirement and below are the filters applied on data

Pandemic Years — 2020–2021 (maximizing the salary expectation)
Geo — Atlanta, Georgia
Industry — Software Publishers
Job Title — As mentioned above permutations and combinations of [Senior/Sr/Analytics/Business Intelligence/Insights/Reports/Datawarehouse]

Inferences

If we take only GA salaries into account for the pandemic years, and within same industry — the salary of $150k-$180k is fair
However if we consider remote jobs , then salary of $150k-$180k is not such a great deal (given that $190k is average)

Note

John took the above data to the employer and negotiated and now he makes $210k base salary

Did past salary matter ?

There are some employers who ask your previous salary history even before you interview. It is a personal choice whether you want to specify (in fact in some states, it is illegal to ask previous salary history). In the above case, John did NOT want to give away his previous salary details UNTIL he knew the work load, his responsibilities and the team he is going to work with. Most of those fears were allayed during the interview process (as he learnt about the humans he was going to collaborate with)

Any other factors ?

Yes absolutely. There were some subjective inferences made by the career coach for John — especially his depth and breadth of hard skills, soft skills like communication, collaboration, amicability etc. It is the cumulative assessment using data and experience of the coach, that helped John. The coach would NOT have been convincing enough to John (neither John would have during his negotiation of the offer) without Data!

Conclusion

If John was not confident of his skills, obviously no amount of data can help. In this case, John knew he was relatively smart and hardworking employee. He just did not want to leave money on the table. He made at least additional $40k-$50k just because he signed up for coaching

Next time you seek coaching or get coached , do ask for Data Driven coaching !

Platforms like Symmetrics are championing open wage metrics & Data Driven coaching at scale. Sign up for a coaching session OR Register as a coach