Taylor W Hickem

Data Scientist

Applied Data Science, Research and Engineering in solutions for resiliant and sustainable cities

My current work is in developing technology solutions for resilient and sustainable cities. I enjoy applying my passion for mathematics, complex abstract topics, programming in python/R/ MATLAB/Javascript/VB and hacking to real world applications using data driven problem solving.

In the past 11 years I have helped manufacturing companies improve bottom line performance with total value >USD 4 million/year through data driven continuous improvement (CI) projects.

I credit my past achievements in driving improvement changes and discovering actionable insights to a combination of background knowledge from STEM fundamentals and communicating through persuasion arguments rich with data visualizations.

The value-add I contribute to the teams I work with is my track record of successful CI research projects for complex ill defined problems, advanced techniques in Data Science, domain specific knowledge in STEM topics and experience field research pilot and prototype validation.

Experience and qualifications

  • statistics and applied mathematics : Linear programming , convex opt, time series, pattern recognition, FFT, graph search, MLE, regression, PCA, forecasting
  • machine learning : gradient descent, MLE, classification, decision trees - practical application using scikit-learn
  • 5+ yrs programming experience - python, R, MATLAB, Javascript, Visual Basic
  • data mining with unstructured data sets and python packages - nltk
  • manufacturing data systems - DCS, SCADA, SQL, smart IoT
  • 14 yrs developing and implementing CI projects for manufacturing, driving change through technical persuasion argument + data visualizations
  • subject matter knowledge in cross-disciplinary range of STEM fields - Chemical Engineering, Electrical Engineering, Chemistry, Physics, Neuroscience, Biology

Data Science

  • Languages : Python, R, MATLAB, VB, SQL
  • Relational database, API and COM interfaces
  • Linear Programming and Convex Optimization
  • Machine Learning
  • System Identification - Regression, PCA
  • Time Series – FFT, Forecasting
  • Nonlinear Modeling and Optimization
  • Linear Control Systems – MPC, DMC
  • Network and Graph Search BFS, DFS, A*

AgriTech

  • Plant biology - Solanaceae family - tomatoes, pepper
  • Hydroponics and food waste composting
  • Engineered systems - Raspberry Pi, LED lighting

Chemical Process Engineering

  • Refinery Unit Operations – Distillation, Cracking, Reforming, Power and Utilities
  • Control system, PID tuning, DMC, DCS
  • Chemical Reaction Kinetic modeling
  • Process simulation - HYSYS, ProII

employment history

Education

2008 BS Chemical Engineering High Honors

Georgia Tech, Atlanta GA USA

Other certifications and qualifications

Applied Data Science specialization

ISO 9001 auditor

Kepner-Tregoe problem solving

Contact information

taylor.hickem@helvaimpact.com

+65 9226 1124 Singapore

+1 305 424 9954 USA


35 Jurong East Avenue 1 #02-05

Singapore 609774

Career achievement highlights

2017 $5 mil ($500k/year annualized) averted capital project by expanding capacity using process improvements for BPA free Organosol capacity by 75% reduction in cycle time for bottleneck step horizontal milling

2017 $1 mil/year new product ready for market through RCA investigation and process improvements to resolve quality variations in BPA free Organosol coatings appearance

2015 data mining, statistics, nonlinear modeling to improve crude feed purchases decisions by developing novel algorithm to predict qualities of steam cracker byproduct

2014 Eliminated bottleneck for crude cracking plant by resolving quality problem in MEROX unit desulfurization capacity using field testing, KPI monitoring program and knowledge expertise operator training, kinetic model and unit operating manual

2012 $2 mil/year energy savings from optimization of steam-to-carbon ratio for SMR hydrogen plant from RCA investigation, data analysis, field experiments and process improvement in flow transmitter instrumentation reliability

2010 Improved precision crude feed purchases by introducing risk-based asphaltene precipitation mixing matrix based on SBN/IN model