Engineering teams in data-driven companies

We aren’t just data-driven, we are data-obsessed. #GoodDataMatters

When patients sign up, we receive their consent to retrieve and aggregate their medical records. Our records platform is built around human-in-the-loop machine learning to take the hundreds of pages of scans and faxes of medical records per patient, digitize them, then extract and organize key medical concepts. For our patients and research partners, we deliver structured, queryable data to help patients identify key information in their records and help research partners find deeper insights that were previously only unlocked by manual, painstaking analysis of records. In each of these three key pillars, data is a first class product that we are obsessed with. Like most companies, we use data to power our product, technical decisions, strategy, and roadmaps to ensure that we are delivering value to our customer; but where we truly differ is that data is the product that we acquire (with consent), refine, and deliver. Our data is used to accelerate research and treatment programs. We believe in the transformational power of data – for patients, researchers, and our team.

“If we can measure it, then we can improve it.”

This is a mantra leadership repeats often and one that permeates throughout our culture. We take a hypothesis-driven approach and look to the data to inform decision making. That means we test and measure as much as we can, whether it’s conducting hundreds of A/B tests, constantly evaluating what search terms job seekers are using, or building and improving a large-scale recommendation system powered by machine learning. Engineers can expect to work closely with our data scientists on projects such as Search Ranking, Employer Recommendations, Budget Optimization and Salary Extraction, and Estimation.

We’re obsessed with using data to inform every decision.

Anything is open for debate, provided the argument is backed by data. To that end, we rely heavily on Segment, Snowflake, Dbt, and Mode for Business Intelligence. We use a combination of LaunchDarkly and Amplitude for A/B testing and Full Story to replay user sessions and uncover UX insights.

Individually, engineers own a set of metrics that they are responsible for analyzing, reporting, and developing a product roadmap against. We also believe in self-serve analytics. Regardless of job title, everyone in the company analyzes data.

We rely heavily on data-driven decision making.

We’re building a wide range of passwordless, frictionless solutions on one platform, with a focus on both security and conversion. By using both data and qualitative user research, we can create the best products for our customers. We’ve invested in telemetry of services from the beginning and have several dashboards in the office that show traction toward goals. Analytics also help inform our site map and information architecture. For instance, we used analytics and research to revamp our onboarding experience.

One of our core engineering values is “Evidence is our North Star.”

At the highest level, the nature of our domain is clinical. Our product is evidence based and backed by industry-leading research and randomized controlled trials. Approximately 10 million people have access to Sleepio and Daylight – our digital therapeutics for insomnia and anxiety. Data informs everything we do as we continue to iterate and improve these offerings for our customers. We build our OKRs based on the data we collect and are constantly looking for ways to improve our DevOps metrics.

Data improves the insurance experience for our brokers and clients.

By running a full-stack brokerage, we sit in a privileged position to analyze data. We collect structured information about risk from clients (e.g. property data, business metrics, etc.) and risk pricing data from insurance markets. This industry is particularly opaque and we have a rare opportunity to provide transparency and build the first ever multi-sided marketplace for risk.

There are lots of thorny technical problems to solve at Newfront, from workflow systems to data modeling, to document generation. We always rely on the data we collect from brokers and clients to inform new projects and test initiatives before release.

Several engineers on our team have backgrounds in data analysis and mathematics, and we encourage the entire team to leverage their expertise when shipping products.

Instacart is a data-driven organization.

We build and test often, and use as much data as we can gather to inform new features for our customers and shoppers. Our data science team in partnership with our product team develops hundreds of experiments per quarter to improve the customer, shopper, retailer, and advertiser experiences across our product portfolio.

If you’re interested in learning more, we’d love to hear from you!