Associate Level 1 / Senior Associate - Data Scientist (Quant Research)

Your work will center on designing and deploying AI solutions for time series forecasting, financial modeling, anomaly detection and other Quant related usecases. We use AI to discover patterns, classify information, and predict likelihoods. Our team works on building, refining, testing, and deploying these models to support various business use cases, ultimately driving business value and innovation.

As a Data Scientist on our team, you can expect to work on challenging projects, collaborate with stakeholders to identify business problems, and have the opportunity to learn and grow with our team. A typical day may involve working on model development, meeting with stakeholders to discuss project requirements/updates, and brainstorming/debugging with colleagues on various technical aspects.

At the Lab, we're passionate about staying at the forefront of AI research, bridging the gap between research & industry to drive innovation and to make a real impact on our businesses.

Responsibilities

1. Develop and maintain AI models on time series & financial date for predictive modelling, including data collection, analysis, feature engineering, model development, evaluation, backtesting and monitoring.

2. Identify areas for model improvement through independent research and analysis, and develop recommendations for updates and enhancements.

3. Working with expert colleagues, Quant and business representatives to examine the results and keep models grounded in reality.

4. Documenting each step of the development and informing decision makers by presenting them options and results.

5. Ensure the integrity and security of data.

6. Provide support for production models delivered by the Mumbai team but potentially as well for other models to any of the Asian/EU/US time zones.

1. Bachelors or Masters degree in a numeric subject with understanding of economics and markets (eg.: Economics with a speciality in Econometrics, Finance, Computer Science, Applied Maths, Engineering, Physics)

2. Knowledge of key concepts in Statistics and Mathematics such as Statistical methods for Machine learning, Probability Theory and Linear Algebra.

3. Knowledge of Monte Carlo Simulations, Bayesian modelling & Causal Inference.

4. Experience with Machine Learning & Deep Learning concepts including data representations, neural network architectures, custom loss functions.

5. Proven track record of building AI models on time-series & financial data.

6. Programming skills?in Python?and knowledge of common numerical and machine-learning packages (like NumPy,?scikit-learn, pandas,?PyTorch, PyMC, statsmodels).

7. Ability to write clear and concise code in python.

8. Intellectually curious and willing to learn challenging concepts daily.

Skills Referential

Behavioural Skills:

Ability to collaborate / Teamwork

Critical thinking

Communication skills - oral & written

Attention to detail / rigor

Transversal Skills:

Analytical Ability

Experience Level

At least 2 years