Internship - Advanced Analytics

Emergent Holdings•Lansing, MI

About The Position

The Data Science team at AF Group, a commercial Property & Casualty insurer, is seeking an intern who's excited to get hands-on experience using machine learning, statistics, and natural language processing to solve complex business problems and help deliver valuable insights to our claims and pricing business partners.

Requirements

Have status as either graduate student or third year undergraduate by the end of the spring term.
Completed at least five courses related to data science, machine learning, statistical modeling, or optimization.
Hold a cumulative GPA of 3.5 or better as of the most recent grading period.
Be able to work full-time during normal business hours for this summer with a start date between mid-May and mid-June.
Should hold or be pursuing a bachelor’s or advanced degree in data science, statistics, mathematics, operations research, engineering, physics, actuarial science, or similar quantitative field.
With proper education and projects (either personal or scholastic), no prior work experience necessary.
Comfortable programming in Python.
Knowledge of SQL with ability to join multiple tables.
Knowledge and experience with all of the following: Linear and logistic regression Gradient-boosted decision trees e.g., LightGBM or similar Neural network K-means clustering
Knowledge of any two or more of the following: Causality modeling: regression discontinuity, matching (e.g. propensity score), meta-learners, etc. Natural language processing: TF-IDF, LDA, word2Vec, BERT, LLM, etc. Time series modeling using either linear model (e.g. ARIMA) or state-space model (e.g. Kalman filter) or neural network model (e.g. LSTM) Advanced clustering methods: T-SNE, Gaussian Mixture, or UMAP Graph data mining and network science Bayesian linear modeling Collaborative filtering or low rank models Mixed-effect modeling Reinforcement learning Linear programming using simplex or similar methods Stochastic processes and Markov chains
Experience with Git repos for version control.
Comfortable with cloud computing platforms like Azure, AWS, GCP.

Responsibilities

Perform exploratory data analysis and feature engineering.
Resolve data-related challenges such as outliers, missing data, imbalanced target, etc.
Build predictive models using supervised and unsupervised learning approaches on structured and unstructured data sets.
Train language models to extract information from notes and scanned documents.
Conduct hyper-parameter tuning and feature selection.
Perform A/B testing.
Provide explainable results on black-box models using SHAP, LIME, or similar techniques.
Evaluate model performance and create exhibits such as lift charts.
Learn about risk, pricing, claims, or actuarial science.
Gain an understanding of insurance and how the business works.
Present work completed during sprint reviews.