Data Scientist Resume Example

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Jessica Claire
  • , , 100 Montgomery St. 10th Floor
  • H: (555) 432-1000
  • C:
  • Date of Birth:
  • India:
  • :
  • single:
Professional Profile
  • 5 Years of experience applying Deep Learning, Machine Learning, Genomics Data Science, Bioinformatics, and Clinical Image Analysis techniques to real-world data and deploy, maintain and integrate novel computational solutions to generate insights for clinically actionable insights in Biotech, Life Sciences, and Digital Health.
  • A talented, creative, and accountable individual with the ability to solve complex problems, work collaboratively, and communicate effectively virtually and in-person with multidisciplinary teams composed of genomics scientists, clinical imaging, scientists, clinicians, surgeons, R&D teams, medical device engineers, data scientists, software engineers, business partners, operations leaders, stakeholders, and clients.
Work Experience
Data Scientist, 05/2020 to Current
Cox Communications Inc Enfield, CT,

Major Projects

  • Using Transcriptome Sequencing (RNA-seq) Genomic Data to Analyze the Differences Between Gene Expression in Fetal and Adult Brains Using Tuxido Tools, R Bioconductor, BioPython, Unix Command Line Tools With 95% Significance Level.
  • Developed a novel method that integrates the patient matched normal and tumor DNA with the RNA to increase the signal-to-noise ratio to identify somatic mutations at low DNA allelic frequencies with the specificity of 98% in endometrial carcinoma and 99% in lung adenocarcinoma with 84% sensitivity.

Key Accomplishments

  • Combine scientific thinking and creativity to overcome analytical challenges with expertise in Bioinformatics, Computational Biology, Genomics Data Science, Big Data, Bioinformatics, Biomedical Informatics, Statistical Genetics, and NGS Datasets with deep knowledge of genome features and annotations such as exonic/intronic/intergenic regions, transcript isoforms, repeats, sequence bias, low-complexity regions, and polymorphic loci.
  • Deploy distributed database technologies (MYSQL, SQL) and big-data analytical tools (Spark, BigQuery, Apache Hadoop/Hive) for mining genetic and genomic datasets from large-scale public genetic databases with high-throughput genetic assays (UK Biobank, ExAC/gnomAD, UK10K, EBI GWAS Catalog, 1KG) and public epigenomic datasets (TCGA, Epigenomic Roadmap, ENCODE).
  • Apply statistical and computational genomics analysis of a wide spectrum of omics and biomarker technologies and datasets, including DNA-based (whole genome sequencing, whole exome sequencing, targeted sequencing), RNA-based (RNA sequencing, microarray), and other biomarker data (proteomics, metabolomics) along with clinical data collected as part of preclinical studies, clinical trials or based on electronic health records to analyze large-scale somatic biological data, such as genotyping SNP, methylation, gene expression, gene copy-number, and small variant detection.
  • Advanced proficiency in computational skills and experience in delivering solutions by managing, visualizing, analyzing, and interpreting NGS, genetic, genomic, and biological data, using R, Python/Jupyter notebooks, Unix/Linux text processing tools, R/Bioconductor, statistical programming packages (SAS, Minitab, MATLAB) to assess data quality, data analysis and create standardized summary tables and figures.
Senior Research and Development Engineer, 05/2019 to 05/2020
Comcast Wilmington, DE,

Major Projects

  • Aortic Blood Flow and Thrombus Auto 3D Reconstruction Image Analysis Using Deep Learning (CNN, UNet Structure, ResNet Blocks, Augmentation Techniques) With 95% Dice Coefficient Accuracy.
  • MRI Brain Tumor Segmentation Image Analysis Using Deep Learning (Encoder-Decoder Based CNN Architecture, ResNet Blocks, Variational Autoencoder (VAE), Augmentation Techniques) With 86% Dice Coefficient Accuracy.

Key Accomplishments

  • Brought understanding of medical device product development and machine learning to build end-to-end cloud-based data ecosystem for Medtronic’s R&D diverse and sensitive data and train models in AWS public cloud.
  • Application of advanced analytics methods (Medical Image Classification, Semantic Segmentation, Deep Learning, CNN, RNN, Computer Vision, Machine Learning Algorithms) to guide portfolio-level decisions efficiently, synthesize complex information into clear insights and translate those insights into decisions and actions.
  • Acted as a thought partner and subject matter expert for applicable advanced analytical methodologies, programs, and projects.
Product Development Engineer, 08/2015 to 05/2019
Penn State University University Park, PA,

Major Projects

  • Design, Development, and Comparative Evaluation of Polymer Based Implants Using Machine Learning (Linear Regression, Support Vector Machines/SVM, Principal Component Analysis/PCA) With 25% Increase in Structural Integrity.
  • Biomechanical Evaluation of Novel Expanding Pedicle Screws Suitable for Osteoporotic Lumbar Spine using Machine Learning (Random Forest, Decision Trees, K-Means Clustering) With 30% Increase in Strength.
  • Structural Health Monitoring of Beam Structures Through Ultrasonic Guided Waves, Wavelet Transform and Computer Vision with 15% increase in accuracy.

Key Accomplishments

  • Combined knowledge of several research domains (implants, next generation products, surgical instrumentation, medical imaging) to advance analytical efforts to support business decisions by designing new machine learning algorithms (SVM, RF, GBM, PCA, Segmentation, Clustering, CNN, RNN).
  • Collaborated with team members and stakeholders to build or improve machine learning, deep learning models, and analytical methodology backing SpineCraft’s experimentation platform to build robust and reliable algorithms that predict medical devices’ performance and risks to the business.
  • Applied machine learning algorithms and visualization dashboards to solve problems across several teams, such as product, operations, healthcare professionals, surgeons, customer support, and compliance within SpineCraft’s agile development environment.
  • Demonstrated self-accountability and looked for opportunities for continuous improvement, develop tools to locate, manipulate, QC, and ensure interoperability of large diverse datasets from across organization with technical and business requirements.
Research and Development Engineer, 08/2013 to 08/2015

Major Project

  • Computational Analysis on a New Expandable Cage for Minimally Invasive Spine Surgery Application With 50% Increase in Anatomic Coverage.

Key Accomplishments

  • R&D in structural health monitoring of structures through ultrasonic guided waves, wavelet transform and computer vision.
  • Adapted machine learning algorithms and computer vision to power the analytics efforts backing the experimentation platform at ECOR Lab.
Technical Skills
  • Machine Learning: Python, R, C, SVM, RF, GBM, PCA, Clustering, NLP, Regression, Predictive Modeling, Recommender Systems, Computer Vision, Tableau, Classification, Clustering
  • Deep Learning: 2D/3D, CNN, Resnet, LSTM/GRU, U-Net
  • Big Data Technologies: Hadoop, AWS, Apache Spark, Hive, HDFS, MapReduce, SQL
  • Python: NumPy & SciPy, Matplotlib, Scikit-Learn, Pandas, Keras, TensorFlow
  • Biomedical Image Analysis in Python.
  • Python for Advanced Data Science.
  • Deep Learning Deployment.
  • Tableau and Visualization.
  • Descriptive Statistics.
  • Inferential Statistics.
  • Advanced SQL.
  • Data Structures
Master of Science: Applied Data Science, Expected in 2023
University Of Michigan - Ann Arbor - Ann Arbor, MI
Master of Science: Mechanical Engineering, Expected in 2013
University Of Toledo - Toledo, OH

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School Attended
  • University Of Michigan - Ann Arbor
  • University Of Toledo
Job Titles Held:
  • Data Scientist
  • Senior Research and Development Engineer
  • Product Development Engineer
  • Research and Development Engineer
  • Master of Science
  • Master of Science