Harris-Stowe State University
Overview
Data Scientist role at Harris-Stowe State University. This is a limited-time position funded by a grant. The successful candidate will analyze data from research on the effects of light pollution on pregnancy using advanced statistical and computational techniques to interpret complex datasets and contribute to understanding environmental impacts on reproductive health. Essential Functions
Strategic Leadership
Train and organize undergraduate researchers. Collaborate with researchers to design experiments and analyze results. Present findings to the research team and at conferences. Stay abreast of industry trends, emerging technologies, and best practices in neurobiology and data science trends and technologies. Program Development And Management
Analyze large datasets related to light pollution and pregnancy outcomes. Develop and implement data models and algorithms. Order supplies associated with the projects data analyses. Lead the planning, design, and launch of new grant related protocols and procedures in line with industry standards. Quality Assurance
Conduct experiments related to light pollution effects on pregnancy, under the guidance of senior researchers. Record, store, and manage experimental data accurately. Ensure compliance with safety and regulatory guidelines. Maintain a clean and organized lab environment. Faculty Support And Development
Assist in the preparation of laboratory reports and presentations. Plan and execute Lab safety and procedure trainings. Provide guidance and support to senior faculty and undergraduate researchers in the development and delivery of all aspects of the grant. Visualize data findings through charts, graphs, and reports. Ensure data integrity and security. Other duties as indicated by the PI of the grant. Minimum Education And Experience
Master’s degree or higher in Data Science, Statistics, Computer Science, Neuroscience or a related field. Experience with statistical software (e.g., R, SAS, SPSS) and programming languages (e.g., Python, SQL). Strong analytical and problem-solving skills. Experience with data visualization tools (e.g., Tableau, Power BI). Excellent communication and teamwork skills. Prior neuroscience laboratory experience preferred. Strong attention to detail and organizational skills. Ability to work independently and as part of a team. Preferred Qualifications
Master’s degree or higher in Data Science, Statistics, Computer Science, Neuroscience or a related field. Knowledge, Skills And Abilities
Knowledge
Neuroscience Fundamentals: Understanding of brain anatomy, neural networks, electrophysiology, neurodevelopment, and neurodegenerative diseases; familiarity with concepts like synaptic plasticity and neural signaling pathways. Biological Data Types: Knowledge of genomic, transcriptomic, proteomic, electrophysiological, imaging (MRI, fMRI, DTI) data, neural spike trains, and behavioral datasets. Statistical Methods: Expertise in statistics applied to neuroscience data, including linear models, Bayesian methods, hypothesis testing, and handling biological variability and noise. Bioinformatics: Familiarity with high-throughput sequencing data analysis, gene expression analysis, and protein-protein interaction networks relevant to neurobiology. Data Ethics and Security: Awareness of ethical considerations, data privacy regulations, and secure handling of biological data. Skills
Programming: Proficiency in Python, R, MATLAB, and Julia; experience with TensorFlow, PyTorch, Pandas, SciPy, and NumPy. Data Wrangling and Preprocessing: Clean, preprocess, and organize large datasets; handle missing data; normalize and prepare imaging data for analysis. Statistical Analysis: Apply advanced techniques using SPSS, SAS, or R for hypothesis testing, regression, and survival analyses in neurobiological contexts. Data Visualization: Create clear visualizations using Matplotlib, Plotly, Seaborn, ggplot2, or D3.js. Neuroimaging Analysis: Analyze MRI/fMRI/EEG data using tools such as FSL, SPM, AFNI, FreeSurfer, or BrainVoyager. Machine Learning Implementation: Develop models for brain signal classification, image segmentation, neural decoding, and predictive modeling of neural activity. Algorithm Development: Build custom algorithms for neurobiological applications. High-Performance Computing: Experience with cloud platforms (AWS, Google Cloud) and HPC environments for large datasets. Abilities
Critical Thinking and Problem-Solving: Interpret complex neurobiological data and identify patterns and potential causative relationships. Interdisciplinary Collaboration: Work with researchers across disciplines and communicate data science concepts to non-technical audiences. Data Interpretation: Interpret statistical and ML results in a neurobiological context. Attention to Detail: Ensure data quality, integrity, and reproducibility. Curiosity and Innovation: Stay updated with current research and technologies to develop innovative approaches. Data Integration: Integrate diverse datasets into unified analyses. Visualization and Communication: Effectively visualize and communicate findings to stakeholders and publications. Adaptability: Learn new tools and methods to address evolving datasets. Please No Phone Calls Due to high application volume, we are unable to accept phone calls or walk-in inquiries about status. Only candidates selected for interviews will be contacted. EOE Statement Harris-Stowe State University is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, age, sex, sexual orientation, gender identity or expression, national origin, genetic information, disability, or protected veteran status. Note
The above statements describe the general nature and level of work performed and are not an exhaustive list of duties and responsibilities. Harris-Stowe State University reserves the right to amend and change responsibilities as necessary. Employment Details
Seniority level: Entry level Employment type: Full-time Job function: Engineering and Information Technology Industries: E-Learning Providers
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Data Scientist role at Harris-Stowe State University. This is a limited-time position funded by a grant. The successful candidate will analyze data from research on the effects of light pollution on pregnancy using advanced statistical and computational techniques to interpret complex datasets and contribute to understanding environmental impacts on reproductive health. Essential Functions
Strategic Leadership
Train and organize undergraduate researchers. Collaborate with researchers to design experiments and analyze results. Present findings to the research team and at conferences. Stay abreast of industry trends, emerging technologies, and best practices in neurobiology and data science trends and technologies. Program Development And Management
Analyze large datasets related to light pollution and pregnancy outcomes. Develop and implement data models and algorithms. Order supplies associated with the projects data analyses. Lead the planning, design, and launch of new grant related protocols and procedures in line with industry standards. Quality Assurance
Conduct experiments related to light pollution effects on pregnancy, under the guidance of senior researchers. Record, store, and manage experimental data accurately. Ensure compliance with safety and regulatory guidelines. Maintain a clean and organized lab environment. Faculty Support And Development
Assist in the preparation of laboratory reports and presentations. Plan and execute Lab safety and procedure trainings. Provide guidance and support to senior faculty and undergraduate researchers in the development and delivery of all aspects of the grant. Visualize data findings through charts, graphs, and reports. Ensure data integrity and security. Other duties as indicated by the PI of the grant. Minimum Education And Experience
Master’s degree or higher in Data Science, Statistics, Computer Science, Neuroscience or a related field. Experience with statistical software (e.g., R, SAS, SPSS) and programming languages (e.g., Python, SQL). Strong analytical and problem-solving skills. Experience with data visualization tools (e.g., Tableau, Power BI). Excellent communication and teamwork skills. Prior neuroscience laboratory experience preferred. Strong attention to detail and organizational skills. Ability to work independently and as part of a team. Preferred Qualifications
Master’s degree or higher in Data Science, Statistics, Computer Science, Neuroscience or a related field. Knowledge, Skills And Abilities
Knowledge
Neuroscience Fundamentals: Understanding of brain anatomy, neural networks, electrophysiology, neurodevelopment, and neurodegenerative diseases; familiarity with concepts like synaptic plasticity and neural signaling pathways. Biological Data Types: Knowledge of genomic, transcriptomic, proteomic, electrophysiological, imaging (MRI, fMRI, DTI) data, neural spike trains, and behavioral datasets. Statistical Methods: Expertise in statistics applied to neuroscience data, including linear models, Bayesian methods, hypothesis testing, and handling biological variability and noise. Bioinformatics: Familiarity with high-throughput sequencing data analysis, gene expression analysis, and protein-protein interaction networks relevant to neurobiology. Data Ethics and Security: Awareness of ethical considerations, data privacy regulations, and secure handling of biological data. Skills
Programming: Proficiency in Python, R, MATLAB, and Julia; experience with TensorFlow, PyTorch, Pandas, SciPy, and NumPy. Data Wrangling and Preprocessing: Clean, preprocess, and organize large datasets; handle missing data; normalize and prepare imaging data for analysis. Statistical Analysis: Apply advanced techniques using SPSS, SAS, or R for hypothesis testing, regression, and survival analyses in neurobiological contexts. Data Visualization: Create clear visualizations using Matplotlib, Plotly, Seaborn, ggplot2, or D3.js. Neuroimaging Analysis: Analyze MRI/fMRI/EEG data using tools such as FSL, SPM, AFNI, FreeSurfer, or BrainVoyager. Machine Learning Implementation: Develop models for brain signal classification, image segmentation, neural decoding, and predictive modeling of neural activity. Algorithm Development: Build custom algorithms for neurobiological applications. High-Performance Computing: Experience with cloud platforms (AWS, Google Cloud) and HPC environments for large datasets. Abilities
Critical Thinking and Problem-Solving: Interpret complex neurobiological data and identify patterns and potential causative relationships. Interdisciplinary Collaboration: Work with researchers across disciplines and communicate data science concepts to non-technical audiences. Data Interpretation: Interpret statistical and ML results in a neurobiological context. Attention to Detail: Ensure data quality, integrity, and reproducibility. Curiosity and Innovation: Stay updated with current research and technologies to develop innovative approaches. Data Integration: Integrate diverse datasets into unified analyses. Visualization and Communication: Effectively visualize and communicate findings to stakeholders and publications. Adaptability: Learn new tools and methods to address evolving datasets. Please No Phone Calls Due to high application volume, we are unable to accept phone calls or walk-in inquiries about status. Only candidates selected for interviews will be contacted. EOE Statement Harris-Stowe State University is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, age, sex, sexual orientation, gender identity or expression, national origin, genetic information, disability, or protected veteran status. Note
The above statements describe the general nature and level of work performed and are not an exhaustive list of duties and responsibilities. Harris-Stowe State University reserves the right to amend and change responsibilities as necessary. Employment Details
Seniority level: Entry level Employment type: Full-time Job function: Engineering and Information Technology Industries: E-Learning Providers
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