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Data Scientist Resume

Long Beach, CA


Detail - driven data scientist with an extensive background in the interpretation, modeling and analysis of complex datasets. Well-versed in a wide range of data science and machine learning techniques. Skilled in the use of mathematical and statistical methods to extract information from data. Proficient in the design and implementation of algorithms in Python.


  • Machine Learning
  • Neural Networks
  • Deep Learning
  • Statistics
  • Time Series Analysis
  • Bayesian Modeling
  • Digital Image Processing
  • Digital Signal Processing
  • Software Development


Confidential, Long Beach, CA

Data Scientist

  • Explore and contribute via consulting to the data science universe and its community
  • International Fellow
  • Writer, Towards Data Science
  • Review Panelist, Google AI Challenge, DataKind
  • Mentor for Deep Learning Specialization
  • Course Beta Tester

Confidential, Mountain View, CA

Data Scientist

  • Member of team that created Confidential Occurrence Rate Data Products
  • Led modeling and analysis of synthetic flux level transit injection experiments
  • Devised and implemented an algorithm that effectively applied k-Nearest Neighbors (kNN) to estimate (for each Confidential target star) the exoplanet detection efficiency as a function of signal-to-noise ratio
  • Created, implemented, and documented software algorithms for Confidential 's Data Processing Pipeline in MATLAB under SVN (Subversion) version control, including:
  • Wavelet-Based Denoiser for the Presearch Data Conditioning (PDC) module
  • Ghost Diagnostic Test and an Ephemeris-Matching code for the Data Validation (DV) module
  • Thruster Firing Events Detector using engineering data for the Photometric Analysis (PA) module

Confidential, Pasadena, CA

Project Staff Scientist

  • SIM Planet Finding Capability Study team
  • In a double-blind test commissioned by Confidential, we successfully demonstrated SIM’s ability to detect Earth-mass planets in habitable zones of nearby solar-type stars, enabling the mission to advance toward consideration for launch by Confidential
  • Spearheaded Confidential team 's analysis and modeling of synthetic astrometric and RV data sets
  • Coordinated consensus on orbit solutions among the five analysis teams
  • Devised and published a novel algorithm to hierarchically detect exoplanets and characterize their orbits from combined astrometric and radial velocity (RV) data sets
  • Developed algorithms to characterize wavefront and line-of-sight jitter for a segmented, actively controlled telescope, from metrology data
  • Performed and presented analysis demonstrating that AMD successfully met customer requirements

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