Entry Level Data Analyst R Programming Jobs in Saint Louis, MO

Harris-Stowe State University is a historically Black institution (HBCU) located in the heart of vibrant mid-town St. Louis, Missouri. Harris-Stowe’s beautiful campus is minutes from the renown Gateway Arch, St. Louis Zoo, St. Louis Art and History Museums, Forest Park and other cultural and educational institutions. Harris-Stowe’s diverse faculty and staff provide a wide range of academic programs to one of the most culturally diverse student bodies in the St. Louis region.

Job Summary:

We are seeking a talented Data Scientist to analyze data from our research on the effects of light pollution on pregnancy. This is a limited-time position funded by a grant. The successful candidate will utilize advanced statistical and computational techniques to interpret complex datasets and contribute to the understanding of 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.
• Excellent communication skills

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: Solid understanding of neurobiology, including knowledge of brain anatomy, neural networks, electrophysiology, neurodevelopment, and neurodegenerative diseases. Familiarity with concepts like synaptic plasticity, brain mapping, and neural signaling pathways.
• Biological Data Types: In-depth knowledge of various data types relevant to neurobiology, such as genomic, transcriptomic, proteomic, and electrophysiological data. Understanding of imaging data (e.g., MRI, fMRI, DTI), neural spike trains, and behavioral datasets.
• Statistical Methods: Expertise in statistics, including linear models, Bayesian methods, hypothesis testing, and statistical significance, specifically applied to neuroscience data. Understanding of how to handle biological variability and noise in data.
• Bioinformatics: Familiarity with bioinformatics, particularly the analysis of high-throughput sequencing data, gene expression analysis, and protein-protein interaction networks relevant to neurobiology.
• Data Ethics and Security: Awareness of the ethical considerations in handling sensitive biological data, especially in human neuroscience research. Understanding data privacy regulations and ensuring the secure handling of medical and genetic data.

Skills

• Programming: Strong programming skills in languages commonly used in data science and neurobiology, such as Python, R, MATLAB, and Julia. Experience with relevant libraries such as TensorFlow, PyTorch, Pandas, SciPy, and NumPy.
• Data Wrangling and Preprocessing: Ability to clean, preprocess, and organize complex and large datasets. This includes handling missing data, normalizing biological data, and preparing imaging data for analysis.
• Statistical Analysis: Skill in applying advanced statistical techniques for analyzing biological datasets. Expertise in tools like SPSS, SAS, or R for conducting hypothesis testing, regression analysis, and survival analysis on neurobiological data.
• Data Visualization: Proficiency in visualizing complex data in meaningful ways to communicate findings. Experience with tools like Matplotlib, Plotly, Seaborn, ggplot2, and D3.js to create graphs, heatmaps, and brain activity maps.
• Neuroimaging Analysis: Skill in analyzing neuroimaging data, such as MRI, fMRI, or EEG data, using tools like FSL, SPM, AFNI, FreeSurfer, or BrainVoyager. Experience with spatial and temporal data interpretation in neuroimaging studies.
• Machine Learning Implementation: Skill in implementing ML algorithms to detect patterns in neurobiological data. Experience in tasks such as brain signal classification, image segmentation, neural decoding, and building predictive models for neural activity.
• Algorithm Development: Ability to develop custom algorithms for specific neurobiological applications, such as detecting neural spikes, simulating neural networks, or classifying brain regions.
• High-Performance Computing: Experience with cloud computing platforms (e.g., AWS, Google Cloud) and high-performance computing (HPC) environments to manage large-scale neurobiological datasets and perform computationally intensive analyses.

Abilities

• Critical Thinking and Problem-Solving: Ability to apply logical reasoning and creative thinking to interpret complex neurobiological data. Capable of identifying patterns, correlations, and potential causative relationships in neural systems.
• Interdisciplinary Collaboration: Ability to collaborate effectively with neuroscientists, biologists, clinicians, and other researchers to translate neurobiological insights into meaningful data-driven conclusions. Strong communication skills to explain data science concepts to non-technical audiences.
• Data Interpretation: Strong ability to interpret the results of statistical analyses and machine learning models within the context of neurobiology. This includes understanding the biological relevance of data patterns and their implications for neuroscience research.
• Attention to Detail: Precision in handling and analyzing large and complex datasets, ensuring data quality, integrity, and reproducibility in all stages of analysis.
• Curiosity and Innovation: A natural curiosity to explore complex neurobiological questions using data-driven approaches. Ability to stay up-to-date with the latest research in neurobiology, machine learning, and computational neuroscience to develop innovative approaches to solving biological problems.
• Data Integration: Ability to integrate diverse datasets (e.g., imaging, genetic, behavioral) into unified analyses to provide a holistic understanding of neurobiological processes.
• Visualization and Communication: Ability to effectively visualize and communicate complex findings to stakeholders, collaborators, and within academic publications. Skilled at tailoring communication to both scientific audiences and non-experts.
• Adaptability: Ability to quickly learn and adapt new tools, software, and analytical methods in response to the evolving field of neurobiology and the growing complexity of available datasets.

"Please No Phone Calls" 

Due to the large number of applications submitted and the high volume of applicant inquiries we receive regarding the status of applications, we are unable to accept phone calls or walk-in inquiries regarding applicant status. Only those 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.

The above statements are intended to describe the general nature and level of work being performed and assigned for this position. This is not an exhaustive list, nor is it limited to all duties and responsibilities associated with the position.  HSSU management reserves the right to amend and change the responsibilities to meet business and organizational needs as necessary.