Data Science Intern
Lewis and Clark Historic National
03.2023 - 07.2023
- Mars 2023
- Worked collaboratively with cross-functional teams to gather requirements and define project objectives
- Cleaned and processed large-scale datasets using pandas and SQL to prepare them for analysis
- Developed and deployed machine learning models to address business challenges, such as customer churn prediction and demand forecasting
- Presented findings and recommendations to stakeholders through clear visualizations and reports.
- Optimized machine learning models for improved prediction accuracy and performance.
- Managed multiple projects simultaneously, demonstrating strong time management and prioritization skills under tight deadlines.
- Collaborated with cross-functional teams for better understanding of business requirements and objectives.
- Implemented data visualization techniques to effectively communicate insights to stakeholders.
- Evaluated model performance using various metrics, ensuring alignment with project goals and stakeholder expectations.
- Enhanced data processing efficiency by automating data collection and preprocessing tasks.
- Identified trends within large datasets, uncovering actionable insights for business growth opportunities.
- Developed custom algorithms to solve unique challenges in data analysis and modeling.
- Actively engaged in continuous learning opportunities – attending seminars, workshops, or webinars to acquire new skills and knowledge in the data science field.
- Participated in team brainstorming sessions to identify innovative solutions for complex problems facing the organization.
- Utilized programming languages such as Python or R extensively throughout the internship – applying relevant libraries and frameworks when appropriate.
- Applied statistical methods for robust analysis of complex datasets in various domains such as marketing, finance, or operations.
- Partnered closely with other departments – bridging gaps between technical nuances of Data Science and specific business needs.
- Conducted extensive research on cutting-edge techniques, staying current with industry advancements in data science.
- Streamlined data pipelines for seamless integration of new data sources into the existing system.
- Designed experiments to test hypotheses and validate model assumptions, refining analytical approaches as needed.
- Provided mentorship and guidance to fellow interns, fostering a collaborative work environment.
- Maintained detailed documentation of project progress, methodologies employed, and results obtained – facilitating knowledge transfer across teams.
- Presented findings and recommendations to senior leadership, influencing strategic decision-making processes.
- Assisted in curating high-quality datasets through rigorous cleaning and validation processes, ensuring reliable inputs for analysis.
- Translated cost and benefits of machine learning technology for non-technical audiences.
- Created data visualization graphics, translating complex data sets into comprehensive visual representations.
- Used SAS, SPSS and Python to manage and analyze large data sets.
- Used rapid application development tactics during programming phases.
- Took notes during meetings to better understand project initiatives and to distribute to stakeholders.
- Performed advanced data extraction and data manipulation.
- Assisted with creating and updating training materials for personnel use.
- Developed and established strong business relationships with both internal personnel and external solution providers.
- Collaborated with business partners to understand business objectives.
- Identified, analyzed and interpreted trends in complex data sets using supervised and unsupervised learning techniques.
- Shadowed database personnel to learn new methods to achieve job duties.
- Maintained schedules of client interactions and project delivery dates.
- Applied appropriate data science techniques to solve business problems.
- Developed and coded software programs, algorithms and automated processes to cleanse and evaluate large datasets from multiple disparate sources.
- Modeled predictions with feature selection algorithms.
- Applied loss functions and variance explanation techniques to compare performance metrics.
- Leveraged mathematical techniques to develop engineering and scientific solutions.
- Developed polished visualizations to share results of data analyses.
- Improved data collection methods by designing surveys, polls and other instruments.
- Helped develop database solutions using multiple SQL languages.
- Pinpointed meaningful insights from large data and metadata sources.
- Created data mining architectures and models to identify trends in large data sets.
- Brainstormed with data personnel to define data modeling standards for projects.
- Participated in workshops to advance skills.
- Performed data administration duties for databases.
- Devised and deployed predictive models using machine learning algorithms to drive business decisions.
- Analyzed large datasets to identify trends and patterns in customer behaviors.
- Compiled, cleaned and manipulated data for proper handling.
- Implemented randomized sampling techniques for optimized surveys.
- Ran statistical analyses within software to process large datasets.
