Data Science problems are everywhere, but the talent is not. At Obviously AI, our vision is to turn every company into an AI company. We do this by providing businesses with access to world class, on-demand data science talent that helps them solve real business problems. On the back end, we empower data scientists with a set of internal groundbreaking tools to help them deliver results in minutes, not months.
We’re a small, scrappy group of people with a strong bent toward failing fast, bias for action and attention to detail. We’re focused on doing the best work of our lives and believe in having a healthy separation of work and life. We keep working hours flexible and are building a hybrid team with most of us located in San Francisco, CA and others working remotely around the world.
Obviously AI is backed by some of the top venture capital firms in the US, and you’ll be on the ground floor of a fast-growing company with a big mission.
About You
As a data scientist, you will be on the ground floor of an incredible opportunity to pair your R and Python mastery with industry’s key and critical business challenges. Obviously AI is currently used by several companies across the globe and our users deeply love the speed, reliability and customer care that we provide.
You will have no shortage of interesting and exciting data science challenges that, when solved, can transform entire businesses around the world.
Our ideal candidate for this role is someone who really wants to get their hands dirty in a small, scrappy team. You should be excited about the idea of solving different data science challenges everyday, educating non-technical users with the right information by boiling down complex AI concepts into layman terms and leading with a lot of autonomy and agency. You’ll report directly to the founders, you’ll be a key proponent of Obviously AI’s early success and have the equity (aka. "skin in the game") to make it worthwhile.
Here are some other qualities we’re looking for in a perfect hire:
- You should have a minimum of 3-5 years of experience in the field of Data Science.
- You have current experience as a hands-on Data Scientist
- You should have practical experience in ML and Data Science methodologies, including Data Preprocessing, Feature Engineering, and Building Forecasting Models (ARIMA, SARIMA, XGBOOST), as well as predictive models (Random Forest, Linear Regression, Boosting, Bagging).
- You should also have experience using ML Libraries such as Pandas, Sklearn/Scikit-learn.
- You are a clear and creative thinker with excellent written and verbal communication skills.
- You can creatively articulate complicated concepts in layman terms.
- You are comfortable with writing R and Python scripts to conduct data analysis, data pre-processing and feature engineering tasks.
- You love machine learning and data science, and are always up to speed with the latest developments in the space.
- You possess the personality to easily connect and become familiar with new people.
- You work collaboratively but autonomously: asking for what you need, but not expecting micromanagement.
- You like processes and want to help build it, but you’re also OK with the "organized chaos" of a small team.
- You’re excited to build a career at an energetic startup, with an eagerness to learn and develop your skill set across a wide range of activities.
- You’re comfortable communicating new ideas and experimenting without fear of failure.
- You’re able to pick up new skills quickly, and adapt well to feedback on your work.
- Interact with customers to explain technical concepts in layman terms.
- Troubleshoot model performance by reviewing model metrics (e.g. precision, recall, f1 score, etc.), exploring and recommending best practices for structuring dataset.
- Perform data cleaning and pre-processing on complex datasets. Incl. removing rows/cols, stripping values, changing units, normalization, imputing missing values, expanding cols, etc.
- Conduct feature engineering on customer datasets. Incl. creating new cols out of existing cols, running statistical functions on rows/cols, working on transforming columns, etc.
- Analyze large amounts of information to discover trends and patterns.
- Build predictive models and machine-learning algorithms (with Obviously Al’s No-Code tool).
- Propose solutions and strategies to business challenges.
- Collaborate with engineering and product development teams.
- Update and manage technical support tickets and requests.
Bonus
- Experience working with small (Seed to Series A) startups.
- Experience working on a data science or AI product.
- Experience working on a self-serve, product-led SaaS product
- Equity Package
- Unlimited PTO
- Training & Development
