Data scientist jobs can differ widely based on the stage of the company you join.
Not all data scientist jobs are built the same and company size matters a lot. Being the first data science hire on a startup is radically different than being part of a Fortune 10 - that much is obvious. But how do you compare different tech companies? Whether you're entry-level or experienced in the data science field, this can be totally opaque.
These companies are plentiful in the Bay Area, luring scientists with the promise of huge scope and freedom of creative expression on how to tackle problems. But beware-joining an an early data scientist may mean that you're doing three, sometimes 4 jobs at once - from basic things like sourcing and cleaning data to setting up frameworks for everyone after you to use.
The hiring bar here is high- most companies don't need a data scientist this early, but the ones that do are often ran by highly credentialed founders with storied resumes from top companies like Google or Airbnb. You'll be expected to work well independently or with very little direction. Often these jobs are filled by industry veterans looking for a new challenge, or that have narrow expertise in the area the startup operates in and wide exposure across programming languages. Expect high workloads, work on weekends and lower base pay than your peers at larger companies(but higher equity upside).
Examples of companies : TortutaAgTech
Job examples: Data Scientist
Job example : Data Scientist
What you’ll do
- Work with a fast-moving engineering team to leverage data collected by our robotic fruit harvesting platform.
- Work with the commercial team to understand what fruit and vegetable growers need and create valuable insights.
- Work with multivariate and ML models to build forecasting models for insights about the robots and the farms where they operate.
- Work heavily with an AWS backend to manage databases, create structure, and prepare data for analysis.
- Travel ~1-2 weeks of the year (US or international in 1-week increments) to ensure successful deployments of features and familiarity with the customer problem.
What we’re looking for
- Required: B.S. in engineering, data science, statistics, etc., plus work experience or an advanced degree in similar fields.
- Required: Exceptional ability to learn, troubleshoot, and problem-solve across software disciplines
- Required: Exceptional communication skills, in engineering and English
- Required: Comfortable with AWS, Python or R, statistical methods, and query languages including NoSQL.
- Required: Experience implementing predictive models and time series algorithms (e.g. probabilistic models, clustering, autoregression).
- Required: Experience with data science frameworks such as Pandas and scikit-learn.
- Preferred: Experience with test plan development and issue-tracking software (e.g. Jira)
Company size : 17 employees use LinkedIn (11-50 company size)
Job Interpretation : The focus of this position is exceptionally strong communication with stakeholders, ability to explain results to audiences with different technical background and deep technical skills.
Companies have to reach a certain size or market penetration before they need to hire a dedicated data scientist to analyze their own data to help with decision making. These job postings are less common for smaller companies but easier to get into than in companies who are building data tools as their main product.
Often one of the founders has filled the role of analyzing data to make business decisions before any job postings are put up when the company is under 100 people. This can be a tough bar to interview for and founders are usually looking for hybrid skillset: data architect + statistician + data scientist with strong data visualization skills and at least a master's degree(yes, we know that's a hefty list). To be successful in a data- driven company, especially as an early employee, you have to be an exceptionally strong communicator. You will also have to think creatively to solve problems - many times you'll be the one driving what tools the company uses.
For larger companies(100-300 people), it's important to distinguish between B2C and B2B companies - the problems you'll be working on at each would be very different.
-B2B companies sell products to enterprise - good examples would be Salesforce and Slack. These companies don't usually handle large data volume, but they pay special attention to data details and data characteristics - these roles focus on depth. The customers are mainly other companies- there's not a lot of them but they will bring in large individual revenue.
- B2C examples would be Doordash and Forward. These companies have massive amounts of individual customers and the data problems are mainly around scale and dealing with large datasets. These roles focus on width, meaning there's very little focus on individual customers (compared to B2B)
A B2B company may have only 10 customers, but each customer may bring in $20k+ per month, while a B2C company may have 300,000 users, but each user can earn a profit of $2 per month.
Make sure you clearly understand the company, its' business model and data issues before you ever set foot in an interview. You should also think about what types of problems you're most passionate about and pick a company that will support your growth not just in your skillset, but also in your industry knowledge.
Company examples : Masterclass
Jon example: Data Scientist at MasterClass
What the successful candidate brings:
- Masters degree in a related quantitative field (Computer Science, Math, Statistics, Engineering, Physics, Economics). PhDs are preferred.
- 4+ years of relevant working experience in a similar role, preferably for an online content, video, subscription or e-commerce property/platform
- Expert experience with data querying languages (e.g. SQL), scripting languages (e.g. Python), and statistical/mathematical programming languages (e.g. R, Matlab)
- Proficiency in using Redshift, PostgreSQL, RDBMS, Hadoop, Spark, or similar technologies
- Expertise with statistical analysis, applying various machine learning techniques, predictive modeling and data mining to solve business problems
- Experience with business intelligence tools (e.g. Looker, Tableau) and web analytics tools (e.g. Amplitude, Google Analytics, Adobe)
- Strategically-minded yet strong detail-orientation
- Proven ability to think creatively, solve problems, learn quickly, work independently, handle ambiguity, and adapt to change in a fast-paced environment
- Excellent written, verbal, and interpersonal communication and presentation skills
- Exemplary relationship builder who is consultative in nature and form partnerships with coworkers and customers
Company size : 290 employees use LinkedIn
Job Interpretation : The focus of this position is on finding someone who can " plug into" an existing process at the company and work as a team player.
Established companies invest significantly in technology and talent to make sense of their own data. Especially in finance, it's not uncommon that these positions are coveted. Many companies such as First Republic Bank have carve-outs in their budget for their data science teams. Sometimes these teams can operate almost like their own startups inside a larger company - almost completely separate from the rest of the org and relied upon for key business intelligence. It's increasingly frequent that companies hire out of market to manage talent costs.
The data science interview at these companies is usually much easier but the requirements around having an advanced degree, like a master's degree or even phd can be much stricter. These companies are more likely to be using Tableau and Excel than Hadoop and many data scientist jobs are more akin to being a business analyst at a tech company. There's very heavy emphasis paid on data visualization, rather than data analytics.
These jobs are really what you make of them - you can coast and do some rudimentary business analytics but if you're very ambitious it's also an amazing opportunity to build build something unique - having millions of customer data can bring unlimited possibilities for data science. Taking Wal-Mart's procurement data as an example, the analysis results will affect the lives of millions of people.
Companies of this size are seldom known for cutting-edge data science solutions, but processing their data sets is still a challenge. Just be mindful - many of these companies have low professional development knowledge for IC data scientist roles - so expect to have to learn politics and deal with some bureaucracy if you want to get ahead. Risk aversion is usually also very high and you might be restricted in what tools you can use due to information security concerns.
Company examples : JP Morgan
Job examples : Data Scientist
Job example: Data Scientist
The Data Scientist is an individual contributor that should be able to apply quantitative, data science and analytical skills to complex problems. He/or she should be able to work across teams to design, develop, and evaluate and execute against those data science and analytical solutions with a keen functional understanding of the business problem. He/she will be responsible for data wrangling, data analysis, modeling, including model selection and producing quick applicable modeling solutions.The Associate will serve as a thought leader including advising on optimal solutions and opining on challenger models. The role works on strategic, highly visible projects firm wide. It is an opportunity to have meaningful impact on a large scale at a leading financial services firm. The role will develop data-centric solutions that move the bottom line for the firm.
In Corporate Technology Machine Learning at JPMorgan Chase, our charter is to solve challenging business problems through machine learning across not only all the corporate functions, but also the four core operating segments of the bank. The challenges involve but are not limited to automating business processes, better decision making and improving operational efficiency. A diverse set of machine learning techniques can be employed for example, decision trees/random forests/GBM; Natural Language Processing and Generation (NLP/NLG); Text Mining.
Company size : More than 55,000 employees use LinkedIn (company size of 10,000+ people)
Job Interpretation : Compared to other jobs, there are much lower requirements around communication - this is a heads down operational role with no decision making authority.
World-class large companies such as Uber, Airbnb, Facebook, and Google have dedicated technical teams led by the industry's top elites. Working at these companies, you will deal with large scale data sets, touch on the most cutting-edge data problems and use the best tools. The talent pool here will really test your data science skills - these roles are considered prestigious and command a high average salary. While they are hard to land, these roles are the career opportunities of a lifetime and can give you the right credentialing to be founder down the line
Company examples : Facebook
Job example: Data Scientist, Product
DATA SCIENTIST, PRODUCT ANALYTICS RESPONSIBILITIES
- Apply your expertise in quantitative analysis, data mining, and the presentation of data to see beyond the numbers and understand how our users interact with both our consumer and business products
- Partner with Product and Engineering teams to solve problems and identify trends and opportunities
- Inform, influence, support, and execute our product decisions and product launches
- The Data Scientist Analytics role has work across the following four areas:
- Product Operations
- Forecasting and setting product team goals
- Designing and evaluating experiments
- Monitoring key product metrics, understanding root causes of changes in metrics
- Building and analyzing dashboards and reports
- Building key data sets to empower operational and exploratory analysis
- Evaluating and defining metrics
- Exploratory Analysis
- Proposing what to build in the next roadmap
- Understanding ecosystems, user behaviors, and long-term trends
- Identifying new levers to help move key metrics
- Building models of user behaviors for analysis or to power production systems
- Product Leadership
- Influencing product teams through presentation of data-based recommendations
- Communicating state of business, experiment results, etc. to product teams
- Spreading best practices to analytics and product teams
- Data Infrastructure
- Working in Hadoop and Hive primarily, sometimes MySQL, Oracle, and Vertica
- Automating analyses and authoring pipelines via SQL and Python based ETL framework
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