Data Scientist

Requirement

Here are some other qualities were 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.

• Responsibilities

• 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 team