By Anmol Rajpurohit.

prepare an analysis and visualization of an unknown data set, while impatient stakeholders watch over your shoulder and ask pointed questions; be prepared to make quantitative arguments about the confidence of the results

prepare an analysis and visualization of an unknown data set, while impatient stakeholders watch over your shoulder and ask pointed questions; be prepared to make quantitative arguments about the confidence of the results describe “loss function” and “regularization term” each in 25 words or less, with a compare/contrast of several examples, and show how to structure a range of trade­offs for model transparency, predictive power, and resource requirements

pitch a reorg proposal to an executive staff session which implies firing some ranking people

interview 3­4 different departments that are hostile to your project, to tease out the metadata for datasets that they’ve been reluctant to release

build, test, and deploy a mission­-critical app with real­time SLAs, efficiently across a 1000+ node cluster

troubleshoot intermittent bugs in somebody else’s code which is at least 2000 lines long, without their assistance

leverage ensemble approaches to enhance a predictive model that you’re working on

work on a deadline in paired programming with people from 3­4 different fields completely disjoint from the work that you’ve done

some opportunities to innovate from a “greenfield” state, but not often

mostly being called into an existing project — which is somehow at risk

having to speak truth to power (not fun, but the essence of the role)

writing code to prepare data

automating process to improve feature engineering and model tournaments

speaking truth to power

learn to leverage the evolving Py data stack: IPython, Pandas, scikit­learn, etc.

learn how to lead an interdisciplinary team

get experience in 1+ domains outside of data/analytics/programming

get a good grounding in design and apply it to data visualization

do everything you can to become a better writer and speaker (outside of academic confs)

participate in meetups; publish blogs, presentations, etc. (hiring managers ignore resumes and look for published content online)

get a good grounding in abstract algebra, Bayesian stats, linear algebra, convex optimization

study up on algorithms and frameworks for streaming data (the bigger use cases on the horizon are not batch)

learn Scalding and functional programming with type safety

avoid Business Intelligence (like the plague)

avoid anything referred to as “The Hadoop Ecosystem” or “Hadoop as an OS”

It is no surprise today that Data Scientist (or related roles such as Data Manager, Statistician, Data Analyst, etc.) is one of the most sought career paths. In response to this cross-industry trend, several top universities have started programs dedicated to Data Science.Allured by the tremendous opportunities, great compensation and visibility to business leaders, many people are moving towards the Data Scientist career path without a thorough careful assessment of the day-to-day responsibilities of such a role, the required attitude; and balance of technical and business skills.In the pursuit to provide data science aspirants a clear realistic picture of the data scientist role, which they can assess against their personality and career ambitions, I recently discussed this with, a data science expert with 25+ years of industry experience. His candid, detailed response is very likely to be an eye-opener for many.Paco Nathan’s short bio is provided at the end of the post.I don’t agree. Not many people have the breadth of skills to perform the role, nor the patience that is absolutely needed to acquire those skills, nor the desire to get there.As a self test:If one doesn’t feel absolutely comfortable performing each of those listed above, right now, then my advice is to avoid “Data Science” as a career.The term Data Scientist was “sexy” as a new role circa 2012 in the sense of DJ Patil, Hilary Mason, et al. However, not everyone gets a chunk of a $4B IPO! (full disclosure: I got invited 3x to join LI prior to their IPO but stubbornly pursued other opportunities; what an excellent team there!)Circa 2012: that was then, this is now. Actual work in Data Science entails:To echo what DJ and others have articulated so well before: most data­related problems are social/organizational (e.g., data silos, lack of metadata, matrix org in­fighting, etc.) or else the key insights probably would have been apparent within that organization already.I have a hunch that much of the interesting work in e­commerce has played out already — big players will continue to reap big revenue, but the work to be done now is mostly outside of Silicon Valley. Or rather, other industries coming here to learn, partner, purchase, etc.For example, Monsanto launched a private equity firm in SF that, practically speaking, can invest more money at more favorable terms into Ag data ventures than just about any VC firm. Meanwhile, VCs in the area have all but ignored data­related ventures in domains that matter — with the exception of Khosla. In the past several months they’ve acquired business units within SV: Climate Corp, Solum, etc., which by the way were funded by Khosla. Expect more of that trend.From my perspective, the big issues in data now are not in ad­tech, but instead real issues: food supply, drought/flooding, energy security, health care, telecom, transportation apart from oil dependency, smarter manufacturing, deforestation monitoring, oceanographic analysis, etc.Also, IT budgets are still enormously flawed w.r.t. data insights. Too much budget goes into the priesthood of “data engineering”, and far too much budget tends to be earmarked for data that’s already cleaned up. Also, I find that the notion of “Product Management” in SV is almost antithetically opposed to effective use of data: in many cases product managers are incentivized to discourage use of data within companies.Hence our value is generally going to be realized at:The first speaks to IT budgets earmarked the wrong way, and the second speaks to Product Management being almost systematically hostile to effective use of data. The third speaks to the fact that several of my biggest contributions as a data scientist have been to provide exec staff with hard evidence to fire other executives and get the company back on track. Again, industry disruptions have impact.For people just starting out, be really careful about where you go to work. If a firm claims to have “excellent engineering” but insufficient use of data circa 2014, then they are *not* the sharpest tools on the workbench; pick some other firm in which to start. Find mentors. Join teams that have strong sponsorship from Finance or Operations (which generally understand data and variance) while perhaps avoiding teams that have sponsorship from Engineering or Marketing (which generally do not understand effective use of data)., not necessarily in order: Paco Nathan is a "player/coach" in the field of Big Data, having led innovative Data teams on large-scale apps for 10+ years. An expert in distributed systems, machine learning, and Enterprise data workflows, Paco is an O'Reilly author and an advisor for several firms including The Data Guild, Mesosphere, Marinexplore, Agromeda, and TagThisCar. Paco received his BS Math Sci and MS Comp Sci degrees from Stanford University, and has 25+ years technology industry experience ranging from Bell Labs to early-stage start-ups.