Data Scientist Lead [Multiple Positions Available]

DESCRIPTION:

Duties: Responsible for identifying key metrics and conducting in-depth analyses to guide business and inform strategy decisions.

Work with cross-functional teams including Product, Sales, Marketing, Account Management, and Risk to help upsell and cross-sell products and to help improve our merchants' experience.

Partner with leaders across the business of Sales, Marketing, Finance and Account Management to deliver high-impact analytics.

Identify and define a business challenge and craft a strategic analysis plan to uncover the necessary insights and solutions.

Collaborate with leaders, various internal teams and stakeholders across the company to develop insights and analytics solutions for both internal and external clients.

Examine diverse data sources and develop statistical models to pinpoint trends and key factors, supporting decision-making in product development, sales strategies, risk management, and operational processes.

Build data visualization by leveraging existing tools or developing new ones, as well as providing ongoing enhancements to business dashboards.

Implement processes and build tools to make data access, extraction, and analysis more efficient.

Build and grow relationships with internal teams to proactively identify business needs and address them using analytics.

QUALIFICATIONS:

Minimum education and experience required: Master's degree in Business Analytics, Data Science, Machine Learning, or related field of study plus 4 years (48 months) of experience in the job offered or as Data Scientist, Decision Analytics Associate, or related occupation.

Skills Required: This position requires four (4) years of experience with the following: Using Python to conduct complex analyses to solve unstructured problems via building predictive models using libraries NumPy, Pandas, Scikit-learn, Scipy, and Statsmodels; predictive modeling techniques, including Logistic Regression and Decision Trees; unsupervised learning using K-Means clustering and elbow method; model interpretability via SHAP; Microsoft Excel (VBA, Vlookup, Index Match, Pivot Tables).

This position requires two (2) years of experience with the following: Customer Churn Predictive Models with supervised learning techniques leveraging XGBoost, Random Forest, and time series forecasting methods ARIMA, Prophet, and LSTM using libraries TensorFlow, Keras, Fbprophet; anomaly detection methods, including Isolation Forest and One-Class SVM; Statistical Analysis and Hypothesis Testing capabilities, including ANOVA and TURF Analysis; handling banking-related datasets, including Customer Complaints Data and Payments Data; Financial Scenario Analysis and Risk Modeling using Monte Carlo simulations and scenario analysis; Dashboard creation and automation using Looker, Tableau, and Power BI; Advanced SQL, including writing CTE, Windows Functions, and Stored Procedures; data pipelines leveraging Cron Jobs, Shell Scripts, and Python Automation; Log parsing and text...