We are a SaaS mobile app for field service workers. We would like an expert to develop and maintain a MySQL script for transforming our raw billing data into MRR (approximately 3K lines of code). Find ways to expedite current reconciliation process with accounting and reducing the complexity of the code base.

Skills Required

• Database administration
• MySQL
• ETL
• Process Optimization

Data sources

• MySQL
• Aria
• CSV

Deliverable

Minimum - maintain current codebase with 99.8%+ accuracy with accounting (with SLA)

Desired - Framework for reduction of complexity for task and automation of manual processes for revenue reconcilliation

Deploy to

MySQL server on AWS

In your proposal, please talk about how you approach MySQL optimization and what specific experience you have that will be valuable in this project.  Would you be comfortable with an SLA agreement if you were on a monthly retainer?

We are a Fintech company helping small and medium-sized businesses get ahead. Our cutting-edge technology, quick application and approval processes, customer-focused delivery, and great service have kept us at the forefront of our industry. We are looking for a data analyst with strong SQL Expertise. The Data Analyst is responsible for “turning data into decision making”; driving data collection and distribution to assist leadership with process solutions and implementing key strategic projects/priorities. This role will collect data and develop a simplistic avenue for others to recreate the data needed. They will work successfully in a cross-functional team of both peers and senior management.

Responsibilities:

• Build dashboards and metrics from various operational systems, mining and interpreting data. Monitor and analyze key metrics, interpret performance and provide insight into the data and metrics.
• The dual ability to design SQL databases on the back end, and presentation layer (Tableau, VB, ACCESS, and XML) on the front end.
• Design, prepare and analyze data: build dashboards eports using SQL Server, Nobel\SQL, Access, advanced Excel, VB Basic etc.
• Build analytical solutions to address Sales business needs and questions. Mine and interpret data and offer recommendations and insight around specific questions.
• Ability to conduct prioritized or ad hoc scenario analysis in support of company initiatives or projects a plus.
• Provide analysis and recommendations to address business needs and to impact decisions.
• Coordinate data preparation, analysis, and updates for various projects.
• Write queries (SQL,) to develop analyses in response to requests from internal customers.
• Experience with CRM platforms (Sales force.com, MS Dynamics) is an advantage.
• Ensure accuracy of results produced as an output of data analysis.

Qualifications:

• Minimum 3 - 5+ years’ SQL experience and problem solving skills.
• Bachelor’s Degree or 2-4 years’ equivalent experience desired.
• Clear understanding of basic call center metrics and performance standards.
• Exceptional verbal and written communication skills; professional/courteous attitude.
• Strong interpersonal and relationship management skills.
• Ability to prioritize, multi-task and maintain flexibility in a fast-paced, sales oriented environment.
• Strong SQL programming skills and MS Office Suite of Products to include: Excel (Advanced-level skills) is required.
• Previous experience working with multiple data sources including flat data files, and web-based interfaces.
• Specific experience in contact center systems\data, web analytics, or sales is a plus.

This project is being offered by a Big 4 professional services firm. In the project, we would like to create a scoring system for companies that reflect a company's "Maturity" and "Buzz".  Maturity is roughly defined as the intrinsic characteristics of a company that signal it is ready for large enterprise engagement.  Buzz is roughly defined as the extrinsic signals that indicate a company is attracting interest/attention from the market. 

For the project, we would like assistance in answering the following questions:

• What factors should be inputs for the Maturity and Buzz scores so that they measure the desired company characteristics?
• Should the Maturity and Buzz scores be absolute measures or be relative to a population, and what are the tradeoffs of each approach?
• How should the scoring factors be combined into a valid, robust quantitative output?
• How should the scoring system be designed to account for data gaps in the input variables?
• How should non-quantitative company attributes (i.e. funding stage) be factored into the scoring system?

The company scores will be used as part of a sensing program to identify potential vendors to examine more closely.

As inputs for the scoring system, we have access to data on a range of company attributes provided by data vendors such as MatterMark, CBI Insights, and Quid.  We have a preference for using MatterMark data given the terms of our subscription. We are open to considering other data sources if it would result in a meaningful improvement in the scoring system.

We also have available a list of approximately 75 companies we have engaged with as vendors and a longer list of 1000-2000 companies that have been identified based on referrals and other poorly defined factors. 

To validate that proposed Maturity and Buzz score formulas are generating the desired output, members of our team can provide feedback on the relative scoring of companies we would expect.

The expected deliverable for this project would be:

• A defined process for transforming available company data into a format that can be used in a scoring formula for Maturity and Buzz, respectively.
• Robust scoring formulas that measure Maturity and Buzz respectively. 

In person meetings may be required in New York or San Francisco, based on candidate's location.

Lux Research is an independent research and advisory firm, providing strategic advice and ongoing intelligence on emerging technologies. Lux has 250 corporate clients, who use the Lux Intelligence Services to scout innovative companies and technology trends and growth areas, for their R&D, corporate venture capital, new business development, and global strategy organizations, for over 20 coverage areas such as Autonomous Systems, Sustainable Building Materials, Alternative Fuels, Solar, Advanced Materials, Water, Industrial Big Data & Analytics, Wearable Electronics, and more. Lux Analysts have deep technical expertise, with acadmic and industry backgrounds in the physical sciences. 

Since 2008, as part of providing company profiles to clients who subscribe to the Intelligence Services, Lux analysts have assembed a unique proprietary database of worldwide companies, mostly startups. Analysts create profiles by engaging technically knowledgeable representatives of the company directly in a one-on-one interview (usually by phone), fact-check and evaluate them against comparable companies, and publish the standardized profiles to clients. 

Because of the particular technical skillsets of our analysts and the linear, sequential publication schedule, our analysts are not typically involved in looking at the data asset as a whole. We are currently looking for a creative data scientist to prototype and explore valuable uses of our dataset that we have not yet determined. As an example, last year a full-time data scientist who is no longer with Lux, took on a quick project to look at whether the "lux take" (a 1-5 point rating that ranges from "strong caution" (negative) to "wait & see" (neutral) to "strong positive" (positive)) that we had given companies 5 years ago had any correlation to actual company outcomes. He found a meaningful correlation, which validated our rating methodology, and we published a white paper about it. 

We envision a roughly 1-month project. The early stage would involve diving in and understanding the Lux methodology and the strengths and limitations of our dataset. We would then expect the data scientist to explore the dataset, with Lux staff being available to answer questions and provide direction in an iterative and frequent feedback loop. The key outputs would be recommendations to Lux, which could take the form of 

• analysis and findings of technology or business trends in the dataset

• suggested ways to visualize and present the company dataset

• new and valuable use cases of the company dataset, both for existing clients as well as new potential customers

• recommendations for ways in which the dataset and research methodology could be improved in the future (new kinds of data to capture, more or less frequent updates, augmentation with other data sources, etc.)

Some sample company profiles are provided with this proposal, along with some basic overview statistics on the overall database.

Background

We are a web development and online marketing e-commerce company with 1.5 million visitors and over 2 million of users.

The problem

Despite the vast majority of our users happily utilizing our free and paid services, there are always a few bad apples in the bunch. Our business, like many e-commerce businesses, has to actively seek friendly fraud (aka chargeback fraud) risk. Friendly fraud occurs when an individual makes a purchase online via their credit or debit card then requests a chargeback from the bank once the goods or services have been consumed. A completed chargeback cancels the original transaction and refunds the individual and the merchant is held accountable regardless of the measures taken to verify the transaction. This harms our business, both our reputation and our bottom line.

Project requirements

We are searching for an analytical algorithm and data modeling set to:

1)      identify the traits, signals, patterns of the customers whom are most likely to do request a chargeback

2)      predict the customers who are most likely to request a chargeback and when

The analytic algorithms and data modeling will allow us to:

1)      feed newly subscribed customer data and purchasing information into the data models and analytical algorithms to produce a prediction result for each new customer

2)      show the prediction result as a report (CSV, excel) on the likelihood of chargeback behavior based on:

  a.       subscriber information

  b.      date of the first purchase

  c.       chargeback timeline: date or days after the first purchase

  d.      confidence level

  e.      the identified traits, signals, and patterns for the chargeback behavior

Upon the completion of the project, you will be provide:

1)      analytical algorithm software module(s) in Python

2)      data modeling module(s) in Python

3)      a simple software application for us to feed the new data into the algorithms and data models for generating the prediction results described above.

Datasets

1)      subscriber information

  a.       user reference ID

  b.      registration timestamp

  c.       last logged in timestamp in using our online service application

2)      purchase information of subscriber

  a.       first purchase timestamp

  b.      number of purchases

  c.       last purchase timestamp

  d.      credit card type (VISA/MC/AMEX, etc…)

  e.      chargeback timestamp (on those who did do the chargeback to us)

  f.        service cancellation timestamp (on those who cancel the subscription service)

3)      we can also provide the geographical/location data of the subscriber, if needed

Additional Information

1.       The dataset provided is somehow considered unbalanced - the percentage of unhappy customers is around 1% of total users. Incorrectly identifying a happy customer as an unhappy customer will negatively impact our revenue stream. Obviously, this is what you need to take into consideration in evaluation metrics of machine learning models – we cannot only focus on the True Positive Rate and results.

2.       The subscription status of users does change over time; we’ll need to figure out when is the best timing to be certain that a user will conduct a chargeback soon. However, if we wait too long for enough evidence to catch unhappy customers, it might be too late.

Please describe your ideas for overcoming challenges listed above in the proposal, or any of relevant projects you have done before, so we can invite you to discuss further with our engineering team.

We have a database of about 1M domain names with about 100 metrics for each of the domains. From this data we are able to determine how much each domain is worth and how much we sold the domain for.

We want to use this data to create an algoritham so we can can input these 100 metrics into specific domains to generate the value to provide us inight into how much we need to pay and for what we can sell the domain for.  

We will share examples of the data after an NDA has been established between us and the Expert. This stage will be determined after the initial interview. 

In your proposal, please describe your process and how you would handle this particular project.

Must be able to meet when needed during United States central time business hours

Also please provide the estimated amount this project will take. 

Must have experience in hosted machine learning such as AWS or Google Cloud Services. Also open to suggestions. 

We need an easy and accurate way to get commissions from the software program that our Broker/Dealer provides (M&O), and then organized into QuickBooks (desktop version) broken down into a few categories and classified by sales representatives.

Below is an outline of the current process.

Commission Detail Process

1. Get Commission Statements from MyView Portal (M&O software) through Edocs' Single Sign On (software through our Broker/Dealer)
2. Download and save to Excel
3. Create Pivot Tables for QuickBooks breakdown-->Put into 3 groups
4. Consolidate commissions onto a single Excel sheet for payroll purposes
5. Enter data from #3 into QuickBooks

You can access two screencasts that walk thru each step of the process: 

https://www.dropbox.com/sh/c9kuztxm95621oo/AAA0KjO_gSFHXJfkLUGxUtKFa?dl=0

Password: M&O2QBe@sy 

Note: In accessing the above information, as a registered provider of services, you are bound by Experfy's Terms, Clause 6 on Confidentiality, https://www.experfy.com/terms

Automation Process

1. There is no API available to extract all data from the Broker/Dealer software.  Therefore, the data will be downloaded manually and saved in Excel format.
2. We will run an algorithm that you will develop.
3. The alogrithm will create an output in QuickBook supported format.
4. We will manually import the QuickBooks supported file into Quickbooks for payroll purposes.

We are open to moving to the online version of QuickBooks, if that helps automate #4.

In your proposal, please provide your detailed approach and what kind of tool you intend to build to help us automate some or all of this process. Please also provide a ballpark estimate of the number of hours this project may take.

I run a management consulting firm focused in the Population Health space.

I am seeking an experienced Microsoft Access user to provide me with training. I am a basic user and looking gain the ability to clean a substantial amount of “dirty" data. I am looking for four to eight hours of training and have a strong preference for Experts in the Boston, Ma area. In your proposal please provide your experience using Access, if you have any past training experience, and a quick synopsis of your training. 

Project Description

We are a company that produces marketing materials through mass customization and web-to-print systems. We operate globally with 6 main markets. Email is one of our main channels to reach our continually growing customer base.

Today, we have the capability to personalized emails product tiles at the customer level based on order / browsing data.

We are looking to resource this project with someone that can serve as an end-to-end advisor for this project.

Requirements description:

We want to build a recommended system that is able to:

·         Recommend the right set of products that will influence customer behavior (we will measure the performance based on incrementality of control vs targeted group): Recommendations that are just based on higher likelihood of buying that product are just not enough.

·         Self-improve over time: The system should be initialized and gather information over time on how to adjust recommendations to optimize performance (weekly or even daily adjustments are required).

·         Scale to product inclusion: The system should accept new products and start optimizing with current recommendations seamlessly.

In addition, this project has as main goal to share knowledge on reinforcement learning techniques so it could be later used on other aspects of the business. This is the first use case and we expect the consultant to be able to share information with Vistaprint that would lead our analyst to add some of these skills to their toolkit.

Task description:

As a consultant, your job would be to architect the design of the system and explain step by step how the system would use customer data, prepare recommendation and self-improve over time. In addition, you need to be able to deliver pseudo code on how the system does each step so our engineers can deploy the solution. This job is for someone that can develop a good partnership relation as we will not only implement the system, but we need to understand the pros / cons of any approach as well as future challenge as it related to data management, score computation etc.

Current state:

We are about to launch a major randomization campaign that will target our entire US customer base on 16 emails. During this time, all browsing, order and basket data will be collected in HDFS.

During the last few months we have researched MDP solutions and we believe this approach would be desirable. While we think this could be a possible solution, we are open to new ideas that will be able to meet the project requirements.

Data:

There are 4 different types of data we have about every customer:

·         Transactional data: Which includes everything related to orders that customer have placed in the past up until the data before of emailing recommendations. The data is grouped by product category and aggregated for first order, last 30 days orders, last 15 months orders, last 3 years orders and last order. For each one, we have total amount spent, item count and order count.

·         Basket data: Many times our customers add items to their carts that do not end up in an order. The basket data contains product category of last 30 days and current item in carts. The data includes only binary data for whether a product is / was in the cart of not.

·         Browsing data: Similarly to transactional data, we have all the information grouped by product category and we have aggregations at the following levels: last session (whenever it was), last 7 days and between 7 and 30 days. For each one, we have number of navigations to that product and click information to specific products.

·         Customer segmentation: We have 5 different customer segmentations with 5 levels at most in each. These customers’ segmentations describe visual patterns on what customers are (Consumer vs Business).

Other data:

Email data: We collect information that is sent in an email to the customer. For this project, we have access to customer id and date (to join back to customer information before email sent). We will have information about product shown, placement, type and discount. In addition, we will collect information about creative used and general email sale.

Response data:

We expect you to come up with an optimal reward function for this project. We will have access to any response associated to the email as well as any subsequent site behavior or transactional data. The data will be streamed directly to HDFS into the recommendation system.

All the data is stored in HDFS and current data for scoring will be available via HBASE.

Model output:

For every email deployment, there will be some input into the recommendation such as how many products will be recommended, what product would be featured in the email hero (thus it cannot be shown as a unique recommendation), general sale for this email, product tiles to be used. The recommendation should output the list of products for each customer (in order to be shown – optional). As one of the requirements for this project is to self-improve recommendation over time, the recommendation should explore with a fraction of the customers with new or old recommendations that could end up outperforming top 1 current recommendation.

Questions that we have:

·         How do you make sure the recommendation is generating incremental responses and not only targeting people with high likelihood of buying?

·         How do you define the reward function? Is there any long term value of the reward function?

·         How do you select fraction for exploring new recommendations? How does the reward function affect all the recommendations? At what rate does the system change with new trends on customer behavior?

·         How does the system allow new products to start being recommended?

We want to productionalize an analysis that we've been running offline. For our testing, we built a flask wrapper around an open source package graciously provided by an academic, which unfortunately was not built to scale well. We need someone that has experience with python performance tuning (ideally some familiarity with Theano and flask), and also AWS deployment experience to help us move this to production.

Deliverable: successful deployment of our microservice on AWS.

Costs: for testing, we expect you'll be creating and destroying some GPU instances - these can get expensive. We can add you to our AWS account or reimburse you for the cost of machines on your account (ideal, so you can adjust security settings more freely).

Company: We are a seed-stage startup working on text analytics.