Wednesday, October 22, 2014

Job Opening at TXU Energy: Statistical Modeling Sr. Analyst

Posting a job for a friend at TXU Energy. If you are interested, please apply HERE.

Statistical Modeling Sr. Analyst (17926)

Requisition Id 17926 - Posted 10/01/2014 - Accounting / Finance - TXU Energy - IRVING, TX

This is a sophisticated Senior Statistical Modeling Analyst position.

MINIMUM REQUIREMENTS: EDUCATION & EXPERIENCE

  • Masters’ degree in statistics/mathematics/econometrics/economics/operations research or related field is strongly preferred
  • Relevant professional certification(s) are a plus.
  • 7+ years of related professional analytical work experience with the following is required:
    • Developing advanced analytical models, such as econometric, forecasting, OLS/logistic regression, neural network, decision trees, clustering, PCA models using SAS;
    • Performing exploratory analysis, hypothesis testing, summarizing and presenting complex business data using advanced SQL techniques and Microsoft office (in particular Excel)
  • 3+ years of experience with customer facing and/or project management methods required
  • There is a strong preference for Statistical forecasting experience from within an electrical utility, finance, telecom, insurance, or other subscription based industry that requires highly sophsticated analytics to manage risk.
  • 3+ years in a strategy consulting and/or market analysis role; strong analytical background is greatly preferred
  • Must be a great team player who has a “can-do” attitude, with a strong desire and ability to achieve results in cooperation with other team members, with a healthy sense of business urgency.

JOB SUMMARY

Within TXU Energy, the Senior Statistical Modeling Analyst job function has been created to undertake policy definition and approval, customer experience definition, strategic positioning, customer characteristics and behavior definition, and other similar functions associated with the Energy Retail work streams.  The Specialist has Lead and subject matter expert responsibility for analyzing and developing improvements to business processes, identifying and facilitating the creation of appropriate reporting and definition around each function, managing and integrating processes relating to operations, and delivering an improved customer experience.

Essential Duties and Responsibilities

  • Establish and maintain methodologies to ensure on-going accuracy and integration of processes
  • Design measurement criteria to monitor the nature of the integration process
  • Implement and manage an effective change management process to ensure smooth transition of new or adapted processes
  • Identify process improvements to enhance TXU Energy effectiveness and efficiency
  • Develop and enforce consistent practice of departmental and company processes & procedures
  • Monitor and assess day-to-day related issues to ensure business practices, policies and procedures are executed in an effective manner
  • Work closely with key personnel to define reporting needs and follow through with strategic partner(s) to develop these tools
  • Develop and present regular and timely reports
  • Integrate and manage data from various systems including CIS, CIS+, Siebel, SAP and external sources.
  • Work closely with TXU Governance, TXU Energy business units and strategic partner to coordinate execution and implementation of new business and/or regulatory requirements
  • Resolve reported issues in a timely and decisive manner, or escalate as appropriate
  • Analyze massive amounts of customer data, such as customer feedback to identify solutions to improve and enhance the customers’ experience
  • Assess, analyze and prioritize new business proposals for operational viability and ensure alignment with stated business objectives
  • Create and maintain ongoing dialogue with strategic partner(s), internal personnel and TXU Governance to ensure a positive, valued relationship that has unambiguous alignment with business initiatives
  • Apply a wide variety of high-level statistical and mathematical techniques to complicated processes for modeling, forecasting, profiling, and other complex analytical problems.
  • Employ statistical knowledge to develop relevant descriptive statistics related to business functions and other characteristics used to segment customers.
  • Actively work with various internal/external teams by providing data expertise, offering original perspectives, and challenging conventional views to better align decision making/perceptions with changing business needs.
  • Respond to questions from business clients regarding any aspect of customer data. Conduct research and analysis to gain a detailed insight and is dedicated to providing clear and credible responses and meeting the expectations and requirements of the business clients.
  • Provide direction and guidance to all levels of employees.
  • Controls budgetary spend for function.
  • Participate in the preparation of components of the function’s long-range strategic, operating, and business and capacity planning.

SCOPE LEVEL

Responsible for leading cross-functional work teams within TXU Energy, External Business Partners and Regulatory workgroups with the goal to greatly improve work processes related to TXU retail customer retention and growth.  Serves as a SME and provides guidance and work direction to team members.  Works under minimal supervision.

SKILLS AND COMPETENCIES

  • Excellent consulting, negotiating, consensus building and conflict resolution skills
  • Extensive communication and teamwork skills with both internal and external customers
  • Ability to work effectively in a rapidly changing environment
  • Experience in facilitating /leading large work groups, process teams and focus groups
  • Advanced Skill in using computer software applications such as MS Visio, Excel, Access and Outlook
  • Able to solve problems guided only by general organization objectives, policies, and goals
  • Strong analytical skills, including root cause analysis
  • Working knowledge of Internet and related emerging technologies used to effectively aggregate/analyze critical business data
  • Excellent Project management skills
  • Skill in developing and evaluating process flows.
  • Expert in financial model development and analysis.
  • Ability to make presentations to all levels of leadership, including Executive Management. 

It is the policy of the company to comply with all employment laws and to afford equal employment opportunity to individuals in all aspects of employment, including in selection for job opportunities, without regard to race, color, religion, sex, national origin, age, disability, genetic information, veteran status, or any other consideration protected by federal, state or local laws.

Tuesday, October 21, 2014

Training, Validation and Test

When developing models for forecasting or data mining (I will write a post about these two terms), we usually slice the data into three pieces, training, validation and test:
  • Training data is used to estimate the parameters. 
  • Validation data is used to select models. 
  • Test data is used to confirm the model performance. 
Here let me use two representative techniques, regression analysis and Artificial Neural Networks (ANN) to illustrate how the process works.

Monday, October 20, 2014

BigDEAL Students Winning Poster Contest of Analytics2014

I'm fortunate to be the father of two adorable kids, Leo and Angela. I remember their first cry, first smile, first "ba-ba" (means Daddy in Chinese) and first crawl. Since my wife has told me many times "no more kids", I guess I have to live with only two kids.

Last month, my first PhD student, Bidong Liu, forwarded me an email from the conference chair of Analytics2014, saying that his poster had won the poster contest of Analytics2014. As a winner, Bidong was invited to Las Vegas to present the work "how does temperature affect electricity demand" with the trip covered by the conference. To make the deal even sweeter, I also sent him to the post conference training "Forecasting Using SAS Software: a Programming Approach" taught by Dr. David Dickey.

Bidong Liu @Analytics2014
While Bidong is the lucky guy getting the treat, the poster is the result of teamwork of several engineering students. The second author is Jiali Liu, another BigDEAL student. She is getting her Master of Science degree in Engineering Management next semester. Her thesis topic is on combining load forecasts. If you are trying to fill your analyst position(s), I highly recommend that you check out her profile.

Now I realize that I have more than two kids: the BigDEAL students are bringing me the joyful moments too, after they have survived through all the tough days under my supervision of course.

Thank you BigDEAL students, and congratulations Bidong!

Saturday, October 18, 2014

Linear Models and Linear Relationship

In many papers, we can find the statements similar to the one below:
Because linear models can hardly capture the nonlinear relationship between load and temperature, we use Artificial Neural Networks (or other black-box models) in this paper. 
The major conceptual error of the above statement is due to a common misunderstanding that linear models cannot capture nonlinear relationship.

Wednesday, October 15, 2014

Model, Variable, Function and Parameter

My first job was an engineer at an expert-based consulting firm. One of my first tasks was to develop models for a large Investor Owned Utility. I was quite exited when being assigned to this project - I thought it was a good opportunity to show off my modeling skills. During the project kick-off meeting, I realized that I misunderstood the scope of work. The "models" I was asked to develop are circuit models. "Modeling" was simply to draw the lines, fuses, switches and transformers on a distribution engineering software platform based on their physical specifications, which did not involve any math or statistics at all.

The predictive models in energy forecasting are different from the physical models mentioned above. A regression-based load forecasting model, for example, describes the relationship between load and the factors that drive the load. There are three components in such a model:

Job Opening at Tucson Electric Power: Manager of Customer Analytics

Posting a job for a friend at Tucson Electric Power. If you are interested, please file your application through THIS LINK.

Manager, Customer Analytics

Company: Tucson Electric Power
Location: Tucson, AZ
Job Category: Finance
Position Type: Unclassified

Tuesday, October 14, 2014

A Survey on Software Packages for Load Forecasting

I'm preparing a report on load forecasting for National Association of Regulatory Utility Commissioners (NARUC), which will be published by the U.S. Department of Energy as a white paper in 2015. In one of the sections, I'd like to include a wide range of tools and solutions that are being used in practice. Although the case study is on long term load forecasting, I would be happy to include some contents about tools and solutions for short term forecasting as well.

Now I need your help to make this section as valuable as possible. If you are interested in contributing to this white paper, please send me your response by Oct 31, 2014.

Vendors in load forecasting space, please send me a 1-2 page non-commercial description of your product offerings, which should include, but is not limited to:

  • A general overview of the company and your load forecasting products
  • Pros and cons of the products and information about future enhancements
  • Your customer base, i.e., number of customers and major customers
  • References to your technical resources, such as journal/conference papers and white papers, etc.

Utility load forecasters, please kindly let me know by either sending me an email (hongtao01 AT gmail DOT com) or replying to this post:

  • Your name and affiliation 
  • Your main job duties relevant to load forecasting
  • Tools and/or solutions you are using and your opinions about them (SAS, EViews, Itron, Matlab, MS Excel, R, or else?).
If you are tired of writing long emails, I can call you to take notes during the interview. I will acknowledge the ones who have provided the materials that are used in the final report. If you don't want your name to appear in a public document, please let me know as well.

Friday, October 10, 2014

Very Short, Short, Medium and Long Term Load Forecasting

Load forecasting is so fundamental that it is being used across all sectors in the electric power industry for various business applications. Because of the wide spread of its applications, there are many ways to classify the various load forecasts:
  • based on forecast horizon: very short, short, medium and long term load forecasts;
  • based on resolution of the data or updating frequency (these two concepts are different!): hourly, daily, monthly, seasonal and annual load forecasts;
  • based on business needs: operational, planning and retail load forecasts;

Thursday, October 9, 2014

Job Opening at SCE: Demand Forecasting Analyst

Posting another job at SCE. If you are interested, please visit SCE career website and search the job number 71009009 to apply.

Demand Forecasting Analyst (EMS3)(71009009)

Job Posting : Oct 8, 2014, 1:53:46 AM - Oct 23, 2014, 2:59:00 AM
Primary Location: US-CA-Rosemead

Wednesday, October 8, 2014

Forecasting, Forecast and Forecaster

The story starts with my graduate school days. After my PhD defense, Dr. David Dickey, who was one of the members on my doctoral committee, handed me a printout of my draft dissertation. Based on the edits, I can tell that he read it in detail and revised it carefully. One of the many things he suggested was to change "short term load forecaster" to "short term load forecasting system" to represent the solution, or to "short term load forecasting model" to represent the mathematical formula that captures the relationship between load and other explanatory variables.

It was probably the only time I didn't follow his advice. My reason was that some papers in the load forecasting literature used the term "load forecaster" to represent a load forecasting system. For instance, ANNSTLF, a system developed 15 years ago by EPRI, is the acronym of "Artificial Neural Network Short Term Load Forecaster".