Senior Data Scientist

We are looking for an experienced and talented data scientist with excellent analytical, communication, and modeling skills to develop sophisticated models for equity investments. Candidates must have strong experience with Python, Jupyter, Catboost (or equivalent 

boosted regression tree modeling packages). Strong English and teamwork skills are crucial. 

We are looking for someone to lead the team and grow it over time. 

The employee will apply cutting-edge data science and machine learning tools to automate investing decisions based on business fundamentals. Strong proficiency in managing complex data science projects end-to-end is a must:

• Understand the business problem (fundamental equity analysis) - Prepare data and develop models using advanced statistical and machine learning techniques
• Make rapid incremental progress, communicating effectively with meaningful and insightful reports 

Excellent English communication skills are critical, especially around the data science, machine learning, and equity investing domains. This is an excellent opportunity for an ambitious data scientist to apply their depth of knowledge and experience toward building sophisticated machine learning models in finance. 

Responsibilities: 

1. Understand high-level business objectives set by the HQ and deliver research results according to priorities set by the team. 

2. Work closely with a small but very capable and motivated team of data scientists, engineers, and portfolio managers. 

3. Maintain code used in research and ensure results used in reports are reproducible by others on the team. 

4. Consistently deliver high-quality insights on time and within budget. 

5. Provide ongoing feedback to the HQ, helping maximize value derived from investments in research projects. 

Requirements: 

1. Extensive education in mathematics, statistics, computational science, computer science, or a similar field (PhD is a plus but not required). 

2. 5+ years of professional hands-on data science experience. 

3. Mastery of modern data science software tools and practices (SQL, numpy, Pandas, scikit-learn, Jupyter notebooks). 

4. Hands-on familiarity with common software engineering practices and tools (git and GitHub, pull requests and code reviews, unit and integration tests, continuous integration).