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Mid-level Data Scientist role with broad ML/AI skillset attracts many qualified applicants and strong competition.
Core ML and data skills transfer across industries, though fund administration domain knowledge moderately favors finance background.
Explicit 3–4 year requirement plus mandatory ML, Python, SQL and deployment skills increases shortlist filtering stringency.
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Build, train, and deploy machine learning and predictive analytics models to solve complex business problems.
Analyze large structured and unstructured datasets to generate actionable insights and support business objectives.
Collaborate with business stakeholders, data engineers, and development teams to design data-driven solutions and create visualizations or dashboards for effective communication.
3-4 years of professional experience in Data Science, Machine Learning, or Advanced Analytics.
Strong proficiency in Python and experience with relevant libraries such as Pandas, NumPy, Scikit-learn, Matplotlib, Plotly, and Jupyter Notebook.
Experience with SQL and database querying.
Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Mathematics, Artificial Intelligence, Engineering, or a related field.
Experienced in building forecasting, classification, clustering, recommendation, and anomaly detection models.
Comfortable working with large datasets and implementing data preprocessing and wrangling techniques.
Familiar with AI concepts including Generative AI, with hands-on knowledge of advanced tools or frameworks like TensorFlow, PyTorch, Azure AI services, and MLOps being a plus.