Job Description

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 team with most of us located in San Francisco, CA.

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.

Key Responsibilities:

• Data Preparation & Feature Engineering: Work on data cleaning, feature extraction, and exploratory data analysis (EDA) to prepare data for model building.
• Model Building & Training: Build, train, and evaluate machine learning models using techniques such as classification, regression, and time series forecasting.
• Predictive Analytics: Leverage predictive modeling techniques to identify trends and insights from historical data.
• Data Quality Assurance: Ensure that data quality standards are met for all models, clean customer data to ensure its readiness for machine learning.
• Collaborate with Stakeholders: Work closely with business and engineering teams to understand client data and strategic goals, translating them into actionable data science solutions.
• API Integration: Use APIs to retrieve and manipulate data programmatically, including invoking REST APIs using Python libraries (e.g., requests).
• Version Control & Collaboration: Utilize Git and GitHub for versioning and collaborating on code development with the team.
• Deployment: Experience working with Docker for deployment of machine learning models.
  
  

Key Qualifications:

• Experience: 3-5 years of experience in data science or a similar role, with hands-on experience in machine learning model development and data manipulation.
• Libraries & Tools: Proficiency with data science libraries such as NumPy, Pandas, Scikit-learn, Matplotlib, Plotly, and SHAP.
• SQL Knowledge: Strong understanding of advanced SQL concepts, including joins, grouping, and data aggregation.
• Data Manipulation: Comfort with working on large datasets, transforming, and cleaning tabular data for analysis.
• ML Expertise: Experience with classic machine learning techniques and the ability to work on classification, regression, or time-series problems.
• Code Editor: Proficiency with Jupyter Notebooks or equivalent (e.g., Google Colab) for interactive code development and data analysis.
• Version Control: Familiarity with Git and GitHub for code versioning and collaboration.
• Deployment Experience: Prior experience working with Docker for code deployment and containerization.
• Data Integrity: A keen eye for data quality to ensure models are built with clean, reliable data.
  
  

Preferred Experience:

• Hands-on experience with model building, specifically in areas such as classification, regression, or time series.
• Ability to work with real-world client data and apply data science techniques in practical business settings.