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Join RMDS’s Project Impacter Program to make an impact,
network with 40,000+ data science professionals, and share your knowledge.
Overview
The RMDS Project Impacter (PI) Program is designed to help those interested in Data Science and AI to develop their skills and prepare them for a successful career. The program offers a variety of roles and responsibilities that PI can undertake based on their skills, interests, and professional goals.
Join RMDS’ volunteer & internship program to make an impact, network with 40,000+ data science professionals, and share your knowledge.
Overview
The RMDS volunteer & internship program is designed to help those interested in data science and AI to develop their skills and prepare them for a successful career. The program offers a variety of roles and responsibilities volunteers can undertake based on their skills, interests, and professional goals.
Available Roles
The Global Data Science team is looking for data scientist tech leads to help engineer exciting data science solutions for our various customers. You and your team will cover analytical workflows, feature engineering, modeling, evaluation, and implementation. The individual will work directly with Product Managers, Strategists, Engineers, SME, and other key stakeholders.
Welcome to APPLY!
Job Description:
- Work with Principal Investigator (PI) to gather requirements and design the system for a data science solution
- Work with your team members to use statistical methods, causal inference, machine learning, and decision theory to extract trends, measure results, and make predictions
- Work with your team members to design the system architecture and analytical workflows on different data sources
- Monitor the performance of existing and new solutions to ensure expectations are met
- Work with your team members to assess data and AI maturity and develop analytical solutions
- Work with your team members to develop data science-related training as needed
Qualification:
- Bachelor’s degree or above in quantitative backgrounds such as Data Science, Business Analytics, Applied Mathematics, Computer Science or relevant field
- Experience in data analytics language (SQL, SAS, Python, R, or other)
- Experience in the quantitative role at product company or research organizations working on data science projects
- Ability to translate business needs into analytical frameworks
- Proactive communication and results-driven approach
- At ease working independently and give feedback to both stakeholders and other team members
The Global Data Science team is looking for data scientists and ML engineers to help engineer exciting data science solutions for our various customers. This role will cover analytical workflows, feature engineering, modeling, evaluation, and implementation. The individual will have a dotted line with Product Managers, Strategists, Engineers, SME, and other key stakeholders.
Welcome to APPLY!
Job Description:
- Deliver insights and recommend product enhancements (e.g., trends, patterns, relationships, opportunities) by leveraging a deep understanding of data, customers, and business
- Build analytical workflows on different data sources
- Apply statistical and machine learning models to solve business problems
- Monitor the performance of existing and new solutions to ensure expectations are met
- Build and optimize risk solutions, recommender system, user scoring, and other projects
- Work with team to assess data and AI maturity and develop analytical solutions
- Develop data science-related courses and may deliver the courses when needed
Qualification:
- Bachelor’s degree or above in quantitative backgrounds such as Data Science, Business Analytics, Applied Mathematics, Computer Science, or relevant field
- Proficiency in data analytics language (SQL, SAS, Python, R, or other)
- Experience in data science projects
- Proactive communication and results-driven approach
- At ease working independently or as part of a team
The Global Data Science team is looking for data scientists and data engineers to help data science team exciting DS solutions. This role will cover data connections, data architecting, feature engineering, modeling, evaluation, implementation and results execution.
Job Description:
- Responsible for data preparation, data cleaning and wrangling.
- Responsible for feature engineering.
- Proactively drive the data engineering vision and define and execute on a plan to achieve that vision.
- Design and implement scalable, performant data pipelines, data services, and data products.
- Design well architected data models and define data warehousing best practices.
- Provide support directly to developers using API.
- Able to write and optimize advanced SQL queries in a business environment with large-scale, complex datasets.
- Apply statistical and machine learning models to solve business problems.
Qualification:
- Bachelor’s degree or above in quantitative backgrounds such as Data Science, Business Analytics, Applied Mathematics, Computer Science or relevant fields.
- Expert in Python, SQL and other languages.
- Exposure to data warehouse technologies such as Snowflake, as well as to reporting and dashboarding tools like Looker and Tableau.
- Proactive communication and results-driven approach.
- At ease working independently and give feedback to both stakeholders and other team members.
Are you interested?
RMDS Lab is looking for a motivated and energetic product analyst to assist us in recommending the best products and launch strategies to increase company profitability. The Product Analyst’s responsibilities include working closely with different departments within the company, analyzing product data to make product recommendations, interviewing customers to receive customer feedback, and compiling product data. You should be able to study products on the market in order to create a better product.
Job Description:
- Evaluate the company products and compare them against industry trends.
- Provide assistance with product and rating reviews.
- Analyze metrics to continually improve company products.
- Assist the company in achieving short and long-term goals relating to product growth.
- Work with other company departments to improve the analysis and presentation of products.
Qualification:
- Bachelor’s Degree or above in Business Analytics, Mathematics, or any related field.
- Previous product analysis experience is preferred.
- Familiar with SQL and Python.
- Extensive knowledge of Amplitude, Google Analytics, or Heap is preferred.
- Strong communication skills.
- The ability to work under pressure and adapt to change.
- The ability to balance customer needs against the company’s vision.
- Excellent time management skills.
Are you interested?
The Global Data Science team is looking for data management specialists to help the data science team and database management team to do the database modeling, table design and structuring, data quality analysis, and database maintenance. Data management specialists will be more focused on working with analytics teams to build data pipelines and prepare data for analysis.
Job Description:
- Experience writing and optimizing advanced SQL queries in a business environment with large-scale, complex datasets.
- Experience in data architecture, databases (e.g., MySQL, Oracle, PostgreSQL, DynamoDB, RDS Aurora) design.
- Effectively, efficiently and accurately collect, analyze and report institutional data to internal and external constituents.
- Be responsible for data quality control, performing regular audits to ensure data integrity.
- Create accurate data files for use in reporting information to internal and external audiences.
- Develop, use and maintain query tools to access databases to extract and prepare data.
Qualification:
- Bachelor’s degree or above in quantitative backgrounds such as Data Science, Business Analytics, Applied Mathematics, Computer Science or relevant fields.
- Previous product analysis experience is preferred.
- Experience in data analytics language (SQL, Python, R, or other).
- Experience in the database management role at product companies or research organizations working on data science projects.
- Ability to translate business needs into analytical frameworks.
- Proactive communication and results-driven approach.
- At ease working independently and give feedback to both stakeholders and other team members.
Are you interested?
RMDS Lab is seeking enthusiastic undergraduate and graduate students across the United States and Canada to become campus ambassadors. As an ambassador, you will serve as a bridge between RMDS and your campus peers. You will connect with like-minded students on campus who share an interest in RMDS’ services. You will also help to grow the community on our online platform for Data Science and AI. Working remotely with collaboration and support from RMDS’s marketing and other teams.
Qualification:
- Enrolled as a student at an accredited college or university
- Motivated, friendly, creative, problem-solving, self-aware, passionate
- Effective communicator who enjoys connecting and sharing with others
- Experience in sales, marketing or related field
- Experienced with social media: LinkedIn, Instagram, Facebook, WeChat
- Excellent organizational skills.
- Make a commitment to work at least 3 hours weekly
- A GPA of 3.0 or higher
- Involved in student group/organizations is preferred but not required
Want to join us?
For more information, please go to https://rmdslab.com/campus-ambassadors
Benefits
Free access or discount to a diverse array of training products
Resume building and work opportunity
Recommendation from industry experts – RMDS & Partners
One-month complimentary premium membership
Get professional team guidance and mentorship
Make new friends and network with industry professionals
Data Science Certification
Meet RMDS Outstanding Project Impacters
Yen-Chen Chou
University of Southern California
Data Science Project Impacter
- Built and standardized ETL module for recommendation system focus on collaborative filtering and saved at least 50% data fetching time.
- Created a recommendation system offline evaluation feature, including parameter tuning and modeling evaluation table for different business scenarios; trained collaborative filtering models using nearest neighbor methods and SVD using the created platform.
- Led a team to build COVID-19 risk score calculator for Los Angeles County and merge results to LA County COVID-19 risk map.
- Expanded and optimized COVID-19 risk score ETL pipeline with Point of interest (POI) data, footprint data, and LA county health data; reduced 90% time on data collection process with web scraping and hashing functions.
Shravani Kasralikar
University of California, Santa Cruz
Data Science Project Impacter
I really enjoyed working on the COVID Risk Score project for RMDS! I performed data analytics and also acted as a tech lead for the data collection and processing automation portion of the project. I learned much about management, how to create a deliverable product and how to make a product reproducible for future customer usage. Overall being a Data Science Impacter for RMDS has been a very fulfilling experience!
Shanshan Li
University of Southern California
Data Science Project Impacter
Through the COVID-19 risk score project, I have developed strong teamwork skills and gained hands-on experience to work with data to solve real-world problems and drive meaningful outcomes. And it was a great opportunity to gain experience on tuning complex machine learning models and creating data pipeline automation. Overall, I got a better understanding of the data science project lifecycle.
Sue Suyeon Ryu
University of Southern California (USC)
Data Science Project Impacter
The project coronavirus which RMDS data scientists are bringing their capabilities and knowledge together to provide meaningful understandings of the SARS-CoV-2 to society. My role was researching and providing initial assessments of the epidemiological SIR prediction model that RMDS lab built. It was such a valuable experience for me to be working with these great data scientists, and I felt energized being involved in this project as everyone is unbelievably passionate about what they do.
Ari Falkne
DoorDash, Bird, etc.
Data Engineer Volunteer
I enjoyed learning about stochastic modeling, particularly the MCMC. I did data ingress. I learned a lot about stats.
Dong Wook (Sam) Ko
University of Southern California (USC)
Data Science Project Impacter
The project coronavirus was a perfect opportunity to not only apply what I have learned in classrooms in solving some real-world problems but also work in a structured setting with people from different backgrounds. I have definitely improved my skill sets from both working on my assigned tasks and learning from what other team members' works. Overall, it was a very rewarding experience to tackle the problem that is affecting the entire world with my knowledge and skills.
Yanzhe Yin
University of Georgia
Data Scientist - Internship
I mainly contribute to the data modeling on creating predictions for COVID-19 county-level positive cases and some data collection processes. I'm impressed by the passion of the whole group and inspired by the ideas of people from different disciplines. Now I'm more familiar with different models and more experienced in collaborating with a large group of people.
Lingrou (Yoki) Wang
University of Southern California
Data Science Project Impacter
I assisted with exploratory data analysis and tried to identify patterns before and after the social distancing policy took place. I also aggregated county-level data with location and demographic information that prepares for data modeling. Last but not least, I provided help in outreach and planning of the data science competition.
Yangzi Zhang
University of Southern California
Data Science Project Impacter
It is a great learning opportunity. Through working in the data analytics team, I had the chance to interact with a lot of talented people, contribute to the project, and continue to learn.
Application Process
1. Apply - fill out registration form [Required]
2. One-on-one online interview if you are qualified [Required, within 7 business days]
3. Small testing task [Optional, will let you know in step 2]
4. If YES, you will be informed within 3 business days
Previous Project Highlights
LA Covid-19 Risk Map
Client: Los Angeles City, Esri, SafeGraph
Challenge: The client seeks to create an innovative solution to determine the risk of exposure to COVID-19 in locations in and around the City of Los Angeles. As the city relaxes "Safer at Home" orders, it wants to help people assess the risks of going to various locations (like their local gym, or supermarket) in real-time.
Solution: This solution is an ensemble of machine learning and statistical epidemiological models and uses data about infections, testing, mobility, and social distancing, comorbidity, socioeconomic factors, and other relevant sources to estimate levels of risk. The resulting interactive map shows community-level and POI-level risk of exposure to COVID-19 in Los Angeles County as well as trend analysis and locations of test centers and hospitals.
Highlights:
- Specialized Project Impacter team: we partnered with the City of Los Angeles, the champion team from USC, and the Project Impacter team to implement the solution
- Quick delivery: crowdsource from the vast talent pool of the RMDS Ecosystem to quickly deliver prototype. Over 400 competitors formed teams led by mentors, and 24 unique prototypes were created
- Results-driven: insights to drive policy making, easy to use interface, reproducible methodology is transferable to other counties and extensible to other pandemics
Client: Los Angeles City, Esri, SafeGraph
Challenge: The client seeks to create an innovative solution to determine the risk of exposure to COVID-19 in locations in and around the City of Los Angeles. As the city relaxes "Safer at Home" orders, it wants to help people assess the risks of going to various locations (like their local gym, or supermarket) in real-time.
Solution: This solution is an ensemble of machine learning and statistical epidemiological models and uses data about infections, testing, mobility, and social distancing, comorbidity, socioeconomic factors, and other relevant sources to estimate levels of risk. The resulting interactive map shows community-level and POI-level risk of exposure to COVID-19 in Los Angeles County as well as trend analysis and locations of test centers and hospitals.
Highlights:
- Specialized Project Impacter team: we partnered with the City of Los Angeles, the champion team from USC, and the Project Impacter team to implement the solution
- Quick delivery: crowdsource from the vast talent pool of the RMDS Ecosystem to quickly deliver prototype. Over 400 competitors formed teams led by mentors, and 24 unique prototypes were created
- Results-driven: insights to drive policy making, easy to use interface, reproducible methodology is transferable to other counties and extensible to other pandemics
KNIME workflow
Client: Center for Geographic Analysis at Harvard University and Future Data Lab
Challenge: The workflow-based data analysis project aims to provide a new approach for efficient data analysis and replicable, reproducible, and expandable research.
Solution: The implementation of KNIME data analysis workflows for selected case studies with government statistics of China, which include population and environment, urban and rural development, green energy transition, and county-level GDP estimates with nighttime light data.
Highlights:
- Specialized Project Impacter team: sourced from RMDS Ecosystem to find KNIME developers with support from the KNIME team
- Results-driven: over 50 KNIME workflows developed with associated data inputs and analytics results
Client: Center for Geographic Analysis at Harvard University and Future Data Lab
Challenge: The workflow-based data analysis project aims to provide a new approach for efficient data analysis and replicable, reproducible, and expandable research.
Solution: The implementation of KNIME data analysis workflows for selected case studies with government statistics of China, which include population and environment, urban and rural development, green energy transition, and county-level GDP estimates with nighttime light data.
Highlights:
- Specialized Project Impacter team: sourced from RMDS Ecosystem to find KNIME developers with support from the KNIME team
- Results-driven: over 50 KNIME workflows developed with associated data inputs and analytics results
Hurricane Forecasting Project
Client: Research group from leading center for robotic exploration of space
Challenge: The client looked to create a more nuanced hurricane forecast solution that serves the needs of insurance companies and improves the existing model used by the National Hurricane Center.
Solution: Using various climatology features and other relevant spatial-temporal data, we developed an algorithm and interactive map visualization to predict changes in hurricane intensity in the next 24 hours as well as associated economic loss using machine learning and statistical models.
Highlights:
- Specialized Project Impacter Team: consultant group of engineers and analysts experienced in the topic and tools, selected from our robust and vast scientific community
- Quick delivery: followed our RM4E and ResearchMap solutions development methodologies to quickly deliver the prototype
- Results-driven: solution is catered toward commercial applications, like insurance companies and coastal businesses
Client: Research group from leading center for robotic exploration of space
Challenge: The client looked to create a more nuanced hurricane forecast solution that serves the needs of insurance companies and improves the existing model used by the National Hurricane Center.
Solution: Using various climatology features and other relevant spatial-temporal data, we developed an algorithm and interactive map visualization to predict changes in hurricane intensity in the next 24 hours as well as associated economic loss using machine learning and statistical models.
Highlights:
- Specialized Project Impacter Team: consultant group of engineers and analysts experienced in the topic and tools, selected from our robust and vast scientific community
- Quick delivery: followed our RM4E and ResearchMap solutions development methodologies to quickly deliver the prototype
- Results-driven: solution is catered toward commercial applications, like insurance companies and coastal businesses
Recommender System
Client: RMDS Lab
Challenge: Connect the data science community with useful tools and services
Solution: The RMDS Ecosystem Platform is a one-stop shop for those working on data-driven projects and a plethora of vendors that specialize in a wide variety of data science tools and services. Depending on the intent of the user, we recommend items related to their projects and areas of interest, recent transaction and search history on RMDS, novel discovery, and other factors. The recommendation algorithms are trained both on internal and publicly-available data to recommend the top products from our vendors.
Highlights:
- Large customer base: large community that began over a decade ago who are mostly data scientists, engineers, researchers, and executivesn
- Discoverability: platform is recognized as one-stop shop for all data science tools and products. We increase your discoverability and help with marketing your product via the recommendation system
- Highly targeted: recommendation system connects the right product or service with the right target customer based on recent and historical browsing / purchase history, user profile and project experience, and external, publicly-available data
- High impact audience: ability to target high impact clients with high impact scores
Client: RMDS Lab
Challenge: Connect the data science community with useful tools and services
Solution: The RMDS Ecosystem Platform is a one-stop shop for those working on data-driven projects and a plethora of vendors that specialize in a wide variety of data science tools and services. Depending on the intent of the user, we recommend items related to their projects and areas of interest, recent transaction and search history on RMDS, novel discovery, and other factors. The recommendation algorithms are trained both on internal and publicly-available data to recommend the top products from our vendors.
Highlights:
- Large customer base: large community that began over a decade ago who are mostly data scientists, engineers, researchers, and executivesn
- Discoverability: platform is recognized as one-stop shop for all data science tools and products. We increase your discoverability and help with marketing your product via the recommendation system
- Highly targeted: recommendation system connects the right product or service with the right target customer based on recent and historical browsing / purchase history, user profile and project experience, and external, publicly-available data
- High impact audience: ability to target high impact clients with high impact scores
User Impact Score
Client: RMDS Lab
Challenge: A common question we hear is “could you recommend a real data scientist?” The Data Science Impact Score does exactly that - using external sources and the RMDS Platform to validate and quantify a data scientist’s impact in their field. The RMDS Platform is a comprehensive ecosystem that captures a data scientist’s peer-reviewed projects, tools, knowledge, courses, and qualifications.
Solution: The RMDS User Impact Score is a system that measures a data scientist’s practical, tangible impact on society through projects, workflows, and other field-relevant interactions. Using public records and platform user data, the Impact Score demonstrates an individual’s power or capacity to cause a positive effect in direct or intangible ways.
Highlights:
- Useful in Industry: practical impact
- Objectivity: valid and objective measure
- Many use cases: whether you’re looking to identify the highest impact consumer segments, best course instructors, project collaborators, new hires, or mentorship
- Multifaceted: five dimensions to the impact score allowing you to see a complete profile of each data scientist
Client: RMDS Lab
Challenge: A common question we hear is “could you recommend a real data scientist?” The Data Science Impact Score does exactly that - using external sources and the RMDS Platform to validate and quantify a data scientist’s impact in their field. The RMDS Platform is a comprehensive ecosystem that captures a data scientist’s peer-reviewed projects, tools, knowledge, courses, and qualifications.
Solution: The RMDS User Impact Score is a system that measures a data scientist’s practical, tangible impact on society through projects, workflows, and other field-relevant interactions. Using public records and platform user data, the Impact Score demonstrates an individual’s power or capacity to cause a positive effect in direct or intangible ways.
Highlights:
- Useful in Industry: practical impact
- Objectivity: valid and objective measure
- Many use cases: whether you’re looking to identify the highest impact consumer segments, best course instructors, project collaborators, new hires, or mentorship
- Multifaceted: five dimensions to the impact score allowing you to see a complete profile of each data scientist
