Senior Associate Data Scientist

Job Responsibilities:

• Develop and deploy machine learning models and generative AI capabilities.
• Design, code, test, and debug applications.
• Collaborate with cross-functional teams to achieve common goals.
• Keep stakeholders informed on development progress and benefits.
• Manage project lifecycle and software development deliverables.
• Develop and implement machine learning models and algorithms to optimize AI operations and enhance system performance.
• Collaborate with cross-functional teams to identify business needs and translate them into data-driven solutions.
• Analyze large datasets to extract actionable insights and support strategic decision-making.
• Design and conduct experiments to validate model performance and improve operational processes.
• Monitor and maintain AI models in production, ensuring they operate effectively and efficiently.
• Mentor junior data scientists and contribute to the development of best practices in data science and AI Ops
• Solve complex problems and handle ambiguity with strong analytical skills.
• Develop insights, methods, or tools using various analytic methods such as causal-model approaches, predictive modeling, regressions, machine learning, time series analysis, etc.
• Handle large amounts of data from multiple and disparate sources, employing advanced Python and SQL techniques to ensure efficiency and accuracy.
• Uphold the highest standards of data integrity and security, aligning with both internal and external regulatory requirements and compliance protocols.

Required qualifications, capabilities, and skills :

• Bachelors or Masters Degree in Finance, Quantitative Finance, Data Science, Economics, or a related field.
• Proficient programming skills in python and knowledge of software engineering best practices
• Strong knowledge of basic data science libraries in Python (NumPy, pandas, scikit-learn, pyspark)
• Understanding of the main deep-learning frameworks such as PyTorch, TensorFlow, Keras
• Experience with Linux and shell scripting and experience with LaTeX
• Solid understanding of traditional data science techniques and experience with data engineer pipelines for big data
• Solid knowledge of RNNs, and LSTMs models.