Summary of business:

We are a private Christian university, serving both traditional on-campus students and non-traditional adult online students. 

Problem:

Adult online classes are five weeks long. This is a short window in which to identify students who are struggling in order to intervene. When a student fails a class, there are financial repercussions, they are less likely to return for another class, and they are less likely to graduate. Identifying these students as early as possible will better serve the students and the university.

Our team has received some formal training on data science, R, and predictive modeling. However, we do not have any real-world experience building predictive models and are seeking someone to act as a guide and walk alongside us during this project. We can do the bulk of the data preparation in-house, with guidance from the expert. The actual model building will be led by the expert with our input.

Expertise:

We are seeking someone who has experience building predictive models and understands the unique challenges of adult online higher education. The ideal candidate will have experience with R and T-SQL. 

Technology stack:

MSSQL, Wherescape RED (data warehouse automation/ETL), Tableau, R 

Data Sources:

Our MSSQL Enterprise Data Warehouse (star schema) contains a great deal of information about each student. This includes general demographics as well as their academic history.

We also have data from our online learning platform including individual assignment grades, student login information, and student behavior within the learning environment. The example file attached provides a rough idea of the types of data we plan to use.

Deliverable:

We are seeking to build an algorithm in R that can run daily and predict the likelihood of eventual failure for each enrolled online student. This algorithm will assign a score to every student/course/day and will write into a MSSQL database.

Location Preference:

We prefer US-based candidates but will consider international candidates who are willing to work during US business hours.

We are looking for an experienced Domo expert to quickly and effectively connect the following technology data sources into domo: (ones in Bold are the most important):

• SMM Custom Blend Platform API (see below for access to SMM platform - no documentation available yet)
• Ambassador API (https://docs.getambassador.com/docs)
• Vindicia Merchant API (https://www.vindicia.com/documents/VindiciaSelect10/Default.htm)
• Facebook
• Facebook Ads
• Instagram
• Google Analytics
• QuickBooks (Possibly)

The details for the 1st 3 connectors are linked above, the rest are pretty standard inside of Domo.

1. We need a quote on how long this may take to ensure that the 3 in bold can be connected via API or other means if necessary. 
2. What database limits if any, are there on those API connectors 
3. Connect them inside of domo. 
4. Build the dashboards out in Domo.  This part will be discussed further if 1-3 can be completed on time and in budget.

ACCESS TO SMM PLATFORM:

https://smmspa.herokuapp.com/

[email protected]

We are a privately held global company that would rank in the top 100 of Fortune 500 rankings, with more than 200,000 people delivering over $38B of annual revenue across a multitude of industries, business and technology areas, and functions. We are looking for a rockstar resource / automation guru to assess and implement automation tools to transform our finance function. These automation efforts will allow us to repurpose time and money that could generate immense savings and drive increased quality by eliminating manual, repetitive, error-prone tasks.

Project Objectives

The objective of this project is to develop robotic process automation pilots for a set of manual, error-prone finance processes. Key data related to each process has been collected from business process owners, including resources needed, hours involved, frequency, and specific tasks targeted for automation. For those participants that advance to Phase 2, virtual sessions with the business process owners will be made available as needed to clarify questions about the finance processes.

Project Structure

• Phase 1 – Open Call for Proposals – Winning participants will be hired to move on to Phase 2.
• Phase 2 – Proof of Concept Development  – Winning participants from Phase 1 will be paid to develop automation Proofs of Concept for specific finance processes. Following evaluation of the Proofs of Concept, Phase 2 winners will be selected and moved onto Phase 3 to scale their output into a pilot. (Timeframe approximately 5-15 business days)
• Phase 3 – Pilot Development – Winning participants from Phase 2 will be given the opportunity to scale their Proof of Concept into a pilot. Participants should submit proposed total cost and fixed hourly rates to be considered for final compensation.

Phase 1 Proposals

As part of your proposal please answer the following questions:

• Describe your experience building and implementing pilots using robotic process automation tools, specifically UiPath?
• What trade-offs must be considered when choosing one automation tool over the other?
• What approach or methodology would you apply to build and implement an automation solution?
• What strategies would you use to interface with business process owners?
• What steps would you take to access and work with confidential data?
• What other lessons learned need to be considered when building automation solutions?

We are a global management consulting firm serving Fortune 500 companies. Our Insights team wants your help to create a new method and tool or template to analyze consumer focus group data. Specifically, our team conducts virtual focus group sessions where up to 500 participants are recruited for a 60 minute online session. The goal of the sessions is to better understand consumer segments and possible actions regarding a product, market, or service.  Each session generates at least 50,000 data points which makes these difficult and time consuming to analyze. 

Project Objectives

The objective of this project is to create a methodology and associated template or tool (Excel, web based, etc) that can be used in a repeatable way to better analyze future focus group sessions. Ultimately we need to find the compelling insights in the data to tell a story to the client, and so we need tools to help us find those more quickly and easily. 

As part of your proposal, using the sample data provided, please answer the following:

• Which analytical approaches could be applied in a repeatable way to identify key insights?

• Which analytical approaches should be used to build consumer segments or personas?

• How might we better analyze free text fields, for example by utilizing NLP tactics to identify good comments?

• What trade-offs must be considered when choosing one algorithm, method, or tool over the other?

• Which technologies should we build or buy to create a functional tool or template for speeding up or enhancing analysis?

• What else is interesting or confounding to you about the sample data itself e.g. format, fields, etc.? 

Project Structure

1. Open call for proposals – In a Powerpoint, Word, or PDF document, please answer the questions above and provide a cost estimate to deliver a proof of concept. Winners will be selected on a contingent basis. Timeline: 1.5 weeks

2. Proofs of Concept – Selected proposals will be built out / developed into functional prototypes, templates, or methods with displayed analytical results.  Timeline: 2 weeks

3.  Refinement – Should the outputs of the first two phases be well received by our leadership, we will work with the submitters to refine the proof of concept. Timeline: 3 weeks

Data Available for Analysis

The sample data (.xlsx format) comes from a virtual focus group session we ran with new parents conducted for a consumer products company entering the baby market.  

We are looking to develop a facial recognition proof of concept and want to have an in depth discussion to vet our approach/assumptions. The ideal person would be able to talk about business problems solved, lessons learned, and would also be able to discuss data dependencies and algorithm selection criteria at a technical level. Successfully deploying a solution in a large enterprise is a plus and domain experience at Facebook, government intelligence agencies, and/or casino security would be a major plus. The project will start with a paid 2 hour conversation and could lead to a significant advisory/build engagement over the next few months.

Context:

We are an early stage tech company focused on combining survey and physical data to create accurate, early forecasts and metrics for a global customer base.

Task:

• A government organization has been in the business of monitoring and forecasting a key economic activity for more than 100 years.  Their monhtly outlook reports have become a critical source of truth to the markets
• The task at hand is to forecast these government reports at both the national and subnational level at least one week ahead of time, refreshed daily.
• Final models will be assessed based on accuracy and earliness across a 10+ year time window (backtesting)

Materials:

• Data are all available at state and national levels on a daily basis for 2003-2016 unless otherwise specified:
• Government  forecasts at the state and national level, at a monthly cadence (this is what is being forecasted in this pilot work)
• Government  end of year actuals at the state and national level (this is what the government is forecasting)
• Survey-based data on a weekly basis that is relevant to the target outcome and available at the state level
• Earth data features at a state and sub-state level for key physical variables; formatted for tabular ingest; hundreds of predictors on a daily basis

Timeframe & Project Plan: 

• Overall Aim for productionalized pilot models by end of July; ideally starting work as soon as possible
• Four week timeline, with assumption of one full time resource
• Weekly milestones and project go-no-go decision at end of week one

Ramp Up | week 1

• 2-3 meetings with the team to understand the domain / challenge
• Data onboarding for all of the materials
• Discussion of workplan
• Data exploration
• Variable selection
• Modeling plan
• Outline of white paper (2-3 pages)
• Very rough, first prototype models (initial results)

Preliminary Models | week 2

• Analysis of early results; backtesting prepared
• Prototype models
• Prioritization for model refinements
• Pre-engineering for putting into production

Model Improvements | week 3

• Revised prototype models
• Updated analysis and backtesting prepared
• Pre-engineering for putting into production
• Draft white paper (2-3 pages)

Model Improvements | week 4

• Final set of changes / permutations
• Placing models into production alongside engineering
• Final QA and backtesting
• White paper finalized (2-3 pages)

Logistics:

Location: Preference for onsite but flexibility for video / remote work; strong preference for roughly in timezone

Engagement: Aim is for full time engagement; 40 hours per week

Tools: R and/or Python in the Scikit-Learn framework; collaborator should be a master of either or both of these frameworks

Modeling: Deep experience in machine learning-based predictive modeling and timeseries; example models where candidate should have long experience with applied work include Random Forest, SVM, Cubist, GBM, etc.

Data engineering: Our team will deliver large, structured data cubes (flat files) for modeling; candidate should be familiar with handling at-scale data challenges; that said, local machine execution should be adequate (no obvious need to distribute or use high performance compute)

Company

We are a Fortune 500 company in the Healthcare sector.  We have two types of projects: “Quality Improvement” and “Research.”  We have a decision tree  that looks like a flow-chart and asks a series of questions to determine one of four decisions/endpoints.

Project

Develop a configurable web-based app that asks questions in the flow-chart and displays the decision to the user.  The app will store the all the responses in a database like MySQL.  The web app would be responsive to mobile browsers.  It would email a confirmation on the results to the user and administrators.

We require simple wireframes or mockups to be designed before the web app is developed.  We prefer the use of Google Material Design (https://material.io/icons/) for the user interface.  We will discuss and evaluate design options prior to final development.

The decision tree may change over time, so we need the ability to change the logic ourselves, going forward.

This project includes a list/database of completed requests and decisions.  This will be used to start a list of projects.

The use of an opensource or otherwise free tool is an option, but the app must be hosted onsite and be secure.

Notes for App Development

1) We need to capture the answers to each question in the series, along with the final determination

2) This project includes a list/database of completed requests and decisions.  This will be used to start a list of projects. Fields include submitter and dates.

3) We will include a text disclaimer that the application cannot cover all scenarios and fact patterns and when in doubt or if disagree with decision reached by the flowchart, the user shoudl request consultation.

4) Include a date-based report option,including number of completed decisions, final decision per flow chart, summary of rationale

5) Include a way to pause and restart the process

6) Include an audit trail that tracks all users

7) When App is launched, refer User to P&P on CQI versus Researcher (App with link to P&P)

8) Create ability to hover over key words and define terms/meaning (e.g., experimental)

9) Thie initial Decision Flowchart includes ten yes/no decision nodes and four outcomes.  This may change over time.

Please provide an estimate of the timeline and budget for this project.

We expect this project to be completed in a maximum of 60 hours. Start and end dates are flexible, as are hours/week.

We are a global consulting firm delivering $38B annual revenue with the world's largest companies. Our Rapid Customer Insights team wants your help to create new tools to analyze consumer research data. Specifically, our team conducts virtual focus group sessions for our clients where 100-500+ research participants are recruited for a 60 minute virtual discussion. The goal of the session is to better understand consumer feelings and possible actions regarding a product, market, or service.  Each session generates at least 50,000 data points which makes these difficult and time consuming to analyze and build into good storytelling slide decks for clients. 

Project Objectives

The objective of this project is to create a template, methodology, or tool (or all of the above) that can be used to better analyze future sessions in a more automated and comprehensive fashion. See attached for sample data. Our aim is to make use of “plug and play” methods for conducting standard analyses in a repeatable way. Ultimately we need to shape the analyses into a story for the client, and so we need methods and tools to more quickly and easily create consumer “personas”, identify highly divergent or convergent answers, evaluate and classify the text, and quickly spot correlations between responses that are not immediately obvious.   

As part of your proposal please answer the following questions:

• Which advanced analytics approaches could be applied to the raw data in a repeatable way to derive interesting insights that can’t already be produced with the tools we have?
• What trade-offs must be considered when choosing one algorithm, method, or tool over the other?
• How might we better handle and analyze free text fields via Natural Language Processing to understand quality, tone, and articulation of consumer responses? 
• What is your approach for turning your recommended data discovery process (using sample data) into a repeatable process for future projects? Please assume the raw data provided here will be formatted the same way for future projects. Assuming this, how could you standardize and automate the discovery phase?  
• Which technologies or methods, including third party vendor tools, could we use or build to create a repeatable process for analyzing this data?
• Using the data provided, what interesting insights can you find in these sample data sets?  
• Optional: please perform an initial analysis of the dataset we provided making use of your recommended methods or tools. Detailed submissions with proof of concept reports included will be more heavily weighted in the proposal evaluation process

Data Available for Analysis

The data from our virtual focus group sessions come in two categories: poll based responses (participants select one or more options) and open ended responses (participants respond with free form text). Participants then are able to “up vote” answers they like or agree with, which generates two additional data points per question: Popularity scores, how often the answer appeared from 0 (never)-100 (always), and Consensus scores, to what degree people agree with the statement from 0 (not at all) to 100 (complete agreement). Other data captured in each session are demographic e.g. age, location, household income, buying preferences, product use preferences, and other screening questions. We also would like to analyze the individual answers themselves using natural language processing methods to determine relative sentiment, degree of articulation, and to identify keywords. Please see attached for sample datasets for evaluation.

Project Structure

Phase 1 – Open call for proposals - Two participants will be selected to move on to Phase 2 and will receive $150 (1st) and $100 (2nd) incentive prizes. Please see attached for detailed project requirements.

Phase 2 – Proof of Concept – Each proposal will be build out / developed into a functional prototype, template, or method with displayed analytical results. Each participant will receive $1000 for their efforts, however only one will move to Phase 3 to refine their work. (Timeframe 5-7 days)

Phase 3 – Refinement – Winning participant will be given the opportunity to refine and enhance their concept, earning up to $3,000 in incentive prizes. 

Seeking to introduce dynamic or value-based pricing into a marketplace-like service. Initially looking for advice, but over time could evolve into a larger project.

We require a pragmatic business and data strategist to inform our analysis of our financial opportunity, to help us design a multi-year evolution of our pricing strategy, and to recommend changes to our underlying data operations.

About us:

We are a multi-hundred million dollar provider of legal services spanning thousands of employees across the US, Europe, and Asia. Our largest business employs experienced lawyers on a flexible basis and matches them to Global 2000 companies in engagements that can span days, months, or years. In this way, it operates like a marketplace.

The problem: Our pricing model has historically used a cost-plus approach dictated by local sales leadership and talent managers. From a business perspective, our long-term vision is to build a pricing capability that responds to market dynamics and better reflects client value. In the short term, we want to increase our competitiveness by adopting a portfolio approach to managing pricing across our global business. From an operational perspective, we need to understand necessary changes to our internal process, technology stack, and sales/transactional data model will enable this evolution, and over what time period.

Data sources:

• Transactional data: We can provide financial and related customer data for engagements with clients dating back several years.
• Customer segmentation: We have multiple customer segments with analysis to prioritize the opportunity to support them.
• Talent data: We have unstructured data about all of our talent.
• Market data: We have compiled some third-party research to benchmark compensation for our talent versus our clients’ alternatives.

Deliverable: For now, the deliverable is largely an advisory service. That may evolve depending on the strategy our executive team elects to pursue, in ways ranging from simple financial analysis up to and including a full consulting engagement producing a customized pricing application and learning algorithm.

Location Preference: Strongly prefer New York

Financial Planning and Analysis (FP&A) professional with experience using Adaptive Insights SaaS for modeling pro forma financial projections for multi-subsidiary business with a multi-national chart of accounts (America's, EMEA, APAC).

Business is a Medical Device company with multiple products across multiple business units operating globally. Model is already architected and built! The requirement is to now update assumptions, pressure test model, create reports and further model Investment ROI scenarios.

Experience in VC, PE, Investment Banking, Valuations and Investment Due Diligence is most welcome!

The company also uses NetSuite ERP. NetSuite experience is also welcome.

Project's short term requirements require Adaptive Insights SAAS modeling experience. So, only apply if you are confident that you can take over the company's Adaptive Insights Model.

Must be able to work flexible hours directly with very demanding CEO. This is NOT a "9 to 5" job. Over 40 hours per week will be required in the first few months.

Remote is possible but prefer on-site or combination. Candidates in San Francisco Bay area preferred.

Possibility of full-time hire with benefits for the right candidate after trial contracting period of 2-3 months.

Must be able to start immediately.