Data scientists that produce data-driven products rule the market
(Editor’s note: This is the first installment of a new monthly series by technical recruiter Adam Keene looking at the top job roles in software development and data management).
Job title: Data Scientist - data-driven product.
Reports to: In organizations where data drives, or is the product, the data scientist can report into any number of people. Typically, they would report into a principal or lead data scientist. In the case of smaller and startup organizations, they might report to a director or manager of data science, or sometimes a chief data scientist.
In a data-driven product organization, the data science team will also work closely with the product manager, head of product or chief product officer, as invariably the data scientists will likely be the biggest contributors to the organization's product designs and ultimate success.
What is a data-driven product? In straightforward terms, a data-driven product is a software, service or platform that is able to solve deeply complex problems by utilizing a number of different machine learning algorithms. These algorithms will vary from the straightforward all the way to much more complex programs that utilize deep learning and artificial intelligence. There isn't an industry where data-driven products aren't becoming mainstream.
Demand for this role: In these data-driven project firms, the product is defined by the quality of data that goes into it and the ability of the said product to create actionable insight through machine learning. Due to this, the data science team is absolutely indispensable.
Top industries hiring for this job: Outside of the blue-chip and corporate businesses making a play in this space (IBM Watson being the best known example), many of these data-driven product companies are at a stage of venture capital funding. Technology startups working with initial seed funding through to Series D late-stage ventures are the most typical employers for this type of data scientist. Industries such as cybersecurity, healthcare, advertising, marketing and insurance are also booming with job opporrtunities.
Responsibilities with this job: This role is a highly collaborative one. The organizations in this space are entirely focused on their product, and as someone who defines the data input, method, algorithm and outcome in the organization, the data scientist does have touchpoints all across the company with both technical and non-technical stakeholders.
The thought process behind, and the contribution of algorithms to the product in order to create, iterate or evolve the product is at the very core of the role. Typically, teams will be nimble with a smaller group of high-caliber scientists, data engineers and software engineers who will sit with or near the product team. If the organization serves an external customer, then it's entirely expected that the data scientist may become a stakeholder liaison during projects.
Required background for this job: Typically, a Masters or a PhD is required for these types of opportunities, and top areas of study would include but are not limited to computer science, mathematics, statistics, physics, and even a specific data science or machine learning course. That said, in some cases businesses will consider more industry-specific applicants as well as those from a Bachelors level as well.
Skills require for this job (technical, business and personal): A data scientist in this area will likely come from a mathematical or statistical background at a Masters or PhD level, though this doesn't preclude anyone from outside of this background. Most organizations in this space will most likely possess an open-source tool-set and infrastructure which would likely include Python, R, Hadoop or SQL, Spark and a scalable AWS or Azure environment that could also extend to Keras or TensorFlow on the deep learning side.
On a personal level, and much like other professionals in this area, the expectation is of someone highly driven, curious and pragmatic, who also comes equipped with business-savvy and great communication skills. Organizations in the product space tend to be nimble and highly iterative. Perfection is something which product organizations reach through the advancement of a minimum viable product and a real understanding of what the company needs to achieve commercially in order to hit its goals and maintain the growth and development of the product. Someone who has an understanding of agile environments will likely be better able to succeed in this fast-paced environment.
Compensation potential for this job: An entry-level, PhD or Master’s graduate would realistically expect a base salary of around $120,000 to $130,000 in New York and much of the east coast. This can rise to around $160,000 to $180,000 once the person becomes a lead data scientist or manager. Highly experienced candidates in the product space can realistically expect $200,000 to $250,000 in the current New York City job market.
Success in this role defined by: This is completely linked to the success of the product. Ultimately, the better a data-driven product can perform its tasks in line with customer expectations, the more successful data science will be in the organization. The company lives and dies by the quality of product it creates, and data science is eternally linked with this.
Advancement opportunities for this job: The progression of a data scientist in a data-driven product environment is similarly aligned to other professionals in this area. The individual will most likely be a hands-on, deeply-involved contributor until they reach a principal level.
After this point, it's really up to the candidate to define their own goals and passions. Nimble, smaller product companies will often get a huge benefit from senior individual contributors, and as such, management isn't a necessary step. The leadership piece is invariably vital, and as such a senior data scientist can absolutely grow into a leadership role all the way to the top of the company.