Job Summary

The Customer Service Manager (CSM) serves as a key representative and executive liaison for Entergy within their designated territory. This role is responsible for building and managing strategic relationships with a portfolio of key commercial, industrial, governmental customer and community stakeholders. CSMs are responsible for strategic outreach, managing a portfolio of managed accounts, community development, promoting company products and services, managing local media, issue resolution, and driving exceptional customer satisfaction. The CSM will work proactively to meet customer needs, support company initiatives, and maintain a focus on continuous improvement in reliability, affordability and safety.

Key Responsibilities

• Customer and Account Management: Build strong relationships with assigned key accounts and conduct proactive customer engagements (including in-person STAR presentations, billing issue resolution, arrears management and service reliability concerns); manage issues related to service expansions, outages, energy efficiency, and renewables; negotiate and coordinate service and contract activities associated with new and existing commercial & industrial customers; and resolve escalated customer issues from the OpCo regulatory agency, executive inquiries, Customer Contact Center, media, etc. Maintain up-to-date account and interaction records in Salesforce.
• Operational and Reliability Improvement: Collaborate with internal teams to meet customer satisfaction targets and develop action plans based on customer feedback and reliability data.
• Sales and Product Promotion: Educate customers on products and services. Identify and refer sales leads, and work to secure agreements (MOUs/LOIs) for new product adoption.
• Community and Public Relations: Engage regularly with community leaders, media, and elected officials to communicate company initiatives. Manage local contributions and represent the company in at least one community organization.
• Safety and Professional Growth: Adhere to all safety protocols, participate in quarterly safety meetings, and contribute to continuous safety improvement. Pursue a personalized learning plan and participate in all required training.

Title: Data Science Interview Preparation

This document outlines key areas to focus on when preparing for a data science interview.

Core Concepts

Statistics and Probability

• Descriptive Statistics: Mean, median, mode, variance, standard deviation.
• Distributions: Normal, Binomial, Poisson. Understanding their properties.
• Hypothesis Testing: Null and alternative hypotheses, p-values, Type I and Type II errors, confidence intervals.
• Bayes' Theorem: Understanding its application in classification.

Linear Algebra and Calculus

• Linear Algebra: Vectors, matrices, dot product, matrix multiplication, eigenvectors, and eigenvalues (especially relevant for PCA).
• Calculus: Derivatives and gradients (crucial for understanding optimization algorithms like Gradient Descent).

Machine Learning Fundamentals

Supervised Learning

• Regression: Linear Regression, Regularization (Lasso, Ridge, Elastic Net). Assumptions of linear models.
• Classification: Logistic Regression, k-Nearest Neighbors (KNN), Support Vector Machines (SVM).
• Tree-Based Methods: Decision Trees, Random Forests, Gradient Boosting Machines (GBM, XGBoost, LightGBM). Understand the bias-variance trade-off in these models.

Unsupervised Learning

• Clustering: K-Means, DBSCAN, Hierarchical Clustering.
• Dimensionality Reduction: Principal Component Analysis (PCA), t-SNE.

Model Evaluation

• Classification Metrics: Accuracy, Precision, Recall, F1-Score, ROC curve, AUC.
• Regression Metrics: MSE, RMSE, MAE, R-squared.
• Cross-Validation: K-fold, Stratified K-fold.

Practical Skills

Programming (Python/R)

• Python Libraries:
• • Pandas: Data manipulation, indexing, merging.
  • NumPy: Array operations.
  • Scikit-learn: Model training and pipeline creation.
  • Matplotlib/Seaborn: Data visualization.
• SQL: Writing complex queries involving JOINS, GROUP BY, window functions.

Data Wrangling and Exploration (EDA)

• Handling missing data (imputation techniques).
• Dealing with outliers.
• Feature scaling (Standardization vs. Normalization).

Behavioral and Business Acumen

• Project Walkthroughs: Be ready to deeply discuss 2-3 significant projects, focusing on challenges, decisions made, and business impact.
• Problem Solving: Practice thinking through ambiguous, real-world business problems and structuring a data science approach.
• Communication: Explain complex ML concepts simply to non-technical stakeholders.

--- For further study, refer to resources like:

1. Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow
2. Introduction to Statistical Learning (ISL)
3. LeetCode/HackerRank for SQL practice