This a part time project that will run for 3-4 months for 80 hours per month

Who we are:

We ar a pioneering technology venture that sits at the nexus of big data, network theory, and the behavioral sciences in a space it defines as “predictive behavioral analytics.” Most operational risk faced by organizations flows from the behavior of people within the enterprise, and the internal “trust dynamics” among those people provides the greatest indicator of potential trouble. We map out those trust dynamics and generates actionable insights into risks before they become manifest. We ar transforming the way the C-suite measures and manages people dynamics in order to drive improved engagement, culture, productivity, and performance.

Who You Are:

We are looking for a person to build our technology suite from ground up. A person who will put his/her own stamp on our culture and brand.You are a leader that inspire people to perform to their fullest capabilities and overcome difficult challenges.You have an existing network in the software community and the ability and reach to build a highly motivated engineering team fast.You are proficient with cloud computing platforms (e.g. Amazon AWS, Microsoft Azure, Google App Engine etc.)Experience with network visualization tools, analytics and dashboard design.You possess strong problem-solving, design, and software development skills.You have proven experience debugging and troubleshooting in complex distributed systems.You have the ability to understand the full stack, from back end algorithms to front end UI/UX.You are passionate to work in an agile environment with high quality bars.You are self-motivated to deliver value to customers, deeply caring about your work both in terms of quality and delivering on time.You apply a pragmatic approach to finding the right balance between correctness and expediency.You are interested in problem solving, and are not intimidated by debugging across complex customer environments.

The Role:

• Develop and execute on over-all product strategy and roadmap.
• Build up the engineering department including back-end and front-end.
• Design and build our cloud-based enterprise data analytics and reporting platform (including on- premises components).
• Drive the design and evolution of the online service.Work with customers to resolve bugs and support issues.
• Interact with our advisory board to report on over-all plan forward.

You By The Numbers:

• 8+ years of professional experience in software development.
• 5+ years of experience with the development of large-scale distributed enterprise SaaS applications.
• Experience with agile development methodology and project management.
• Prior start-up experience.
• Strong problem-solving, design, and software development skills.
• Experience debugging and troubleshooting in complex web environments.
• Great communicator and collaborator.
• Is a Leader. 

1st preference for candidates in greater Boston, Ma area.  2nd preference for candidates based on the east coast of United States.

This project is a continuation of a previous project in which we used Experfy to build a financial forcasting alogrithm for a SaaS platform for dairy producers, bankers and other industry professionals. The platform will provide financial analysis for dairy farms that includes Cash Flow Projections, Actual to Budget Comparisons and Balance Sheet and related financial measurements/reports. These are all intended to boost the business acumen of the user and provide them with improved insight into the financial health of a dairy operation. It can also be used as a tool to develop various “What If?” scenarios, guiding a milk producer or banker to make more informed financial decisions.

The project will begin with information architecture and wireframes, followed by design (to be outsourced elsewhere), and end with a Python-based SaaS platform with user registration and payment system. Since the platform requiremnts are undefined, we will begin with the information architecture phase first.

We are a marketing agency with many clients who sell tickets to live events. We need to enhance our digital data collection and analysis capabilities in order demonstrate the effectiveness of client ticketing campaigns. In addition, we seek to provide more effective targeting through channels like Adwords, Google Local, and Facebook advertising. 

A current bottleneck is that substantial sales are being made through the company’s ticket sales widget, but Google Analytics is showing zero conversions for some sources. Under the hood, we have installed Google Universal Tracking as well as other in-page JavaScript instrumentation, but this is not providing the required information. Another bottleneck is that we cannot track people who buy tickets online as a result of a direct mail campaign if there is a long delay between initial online visit and purchase.

We need a Google Analytics specialist who can help us identify the source of the problems noted above and help us fix them. We are not sure how long the project will take, but we are willing to be flexible in our contract. There are additional projects we may want to tackle after our current problems are fixed.  

Please indicate your typical hourly rate in your response.  

We are calculating trust between employees and teams within organizations and need a way of visualizing the information so that it is easy for management to spot areas of high and low trust.

We are looking for people with experience in various visualization tools with the ability to adapt existing tools as per our requiements or build a new tool from scratch.

Also looking for general predictive analytics, algorithm training and machine learning expertize.

Please refer to attached document for more detail on data sources. 

Prefer bids only from United States. 

We have a Big Data Pipe consisting of Kafka, Storm, and Cassandra and need help optimizing our Kafka enviornment for production.  We currently have a 3 node Kafka cluster that we need to scale.  We're looking for someone with hands on experience with production Kafka operations who can assist us with implementing best practices for the following:

• Kafka Architecture Review
• Set-up and Configuration
• Monitoring Tools
• Replay and Failover Handling
• Redundancy and Replication
• Compression
• Capacity Planning

Vytaliz is a startup out of New York that offers a suite of in-home, on-demand medical services via a mobile app. The service works similar to Uber where patients are linked to the closest Vytaliz nurses in their area, who are deployed to the patients’ location within 30mins to an hour from when a request is put in. During the visits, the patient and nurse are connected to a doctor via videoconference for a complete visit. Vytaliz, via it’s mobile app and 3rd party Electronic Medical Records system, collects certain patient information (basic info, medical history, medications, allergies, etc…). We are seeking the help of an expert in healthcare data analytics for Advisory Services on what patient (or other) data is likely to be the most valuable for us now and in the future. Our medical professionals meet patients face to face and often in their homes or offices and can get a great sense of a patient’s lifestyle in addition to their medical history. Among others, the following are some examples of questions we’re looking to get expert advice on:

What patient data should we be collecting?

• Demographics
• Medical history, allergies, medications
• Lifestyle issues
• Diet
• Psychological wellbeing
• Social issues

Answers to following questions:

• What the best way for us to collect this data (from the patient, on the app, other?)
• Should we aim to integrate into other EMR systems to collect information?
• How to store the data (HIPAA issues)
• How to manage the data
• How to analyze the data and its implications
• How best to make use of the data

Advisor will provide guidance over an extended period.

Objective is to begin constructing a revenue management and pricing analytics system. This project includes Data Infrastructure (automate current data processing), Pricing Analytics (end use of excel), and reporting (publish business KPIs). Details of phase 1 are outlined in the Revenue Management System Roadmap document, which will be released upon selection of a candidate. 

We have a large portfolio of clients with a need for financial and insurance planning.  We possess historical data on our customers that resides mainly in three systems: Drake, SmartOffice, and Infusionsoft. Some of the questions require market research or looking at external data. The objective of this project is to better understand our customers and target market so we may better target them.

We would like a data scientists to analyze our data and provide answers to the following questions. The data can be extrated by our internal resource. The sources of data are indicated either below each section or after each question in brackets.

Client Categorization (A,B,C)

1. Where does the bulk of our income come from: A, B vs. C Clients

2. What the time frames are to bring on each type of client (A, B, C)

3. What is our annual recurring revenue on A list clients vs. B list Clients

4. Social Security Income average for A, B and C List clients

AdvisorServices.com will have revenue data

 

Work situation:

1. Where do they work?

2. Income ranges

3. What is the average retirement age of our clients?

4. In service distribution dates for any clients still working?

5. How many clients and prospects are self employed?

Data in Drake

 

Family Info:

1. How many clients still have a living parent

2. How many of your clients have relatives that live in the area

3. How many parents of clients are in or were in a LTC facility

4. How many clients are divorced

1-3 we have no info. And 4 would be in SmartOffice

 

Personal Information:

1. Hobbies and interests (SmartOffice)

2. How many clients have a college degree (no data)

3. How many have a post graduate degree (no data)

4. Is there any data we are missing or irrelevant to gather on our personal preference sheet (we will provide a copy of the personal preference sheet)

 

Financial Information:

1. What's the average home value of our ideal clients? Do they have a mortgage?

2. How many of the clients have long-term care, or we have had the discussion with them and they have signed the waiver.

3. What % has legal services

4. Total number of investable assets per household

5. How many clients have NGL Funeral Trust, how many have declined.

6. What % of clients have pensions

7. How many clients have more than one 401k plan

All of this data resides in Drake

 

Other Professionals:

1. Number of advisors the client works with (SmartOffice)

2. Do we have the contact info for the executor of our clients (No good data)

 

Marketing/Branding:

1. How many people are opening and listening to Real Wealth (from podcast)

2. How many people are opening and reading the newsletter (Forefield)

3. How many people open emails (Infusionsoft)

4. How many people are receiving emails (Infusionsoft)

5. How many clients and prospects do we have on e-mail list?  Is it accurate and up to date? (Infusionsoft)

6. How many of executors are on our newsletter or have we met with? (no good data)

7. How many clients and prospects visit the website (no good data)

8. How many interactions a prospect has before they commit to coming into the office for an appointment (no good data)

9. How many people are receiving social security direct mail vs. tax direct mail vs. Medicare supplement direct mail from us (AllPro runs demographics)

10. What % of these people we are marketing to actually know who we are and know our brand (no good data)

11. What is the opportunity for automated calling campaign (how many are on do not call list, how many can you call, etc .... )? (used Dialmycall once)

12. How many people in our community do we have phone numbers and emails for (SmartOffice)

13. Where do people live that invest with us and what is the revenue per city (SmartOffice)

14. What % of our tax marketing demographic goes to a big box tax prep, CPA firm, or do it yourself? What are the average price people in our area are paying for taxes? (No good research)

15. What % of our clients and prospects buy their own Medicare supplement vs. what % are offered group benefits? (No good data)

 

Business Metrics:

1. What months is the most business written? (Quickbooks)

2. What% of the clients have AUM, Annuities, Life Insurance (SmartOffice, but not accurate)

3. What% of our clients do taxes with us (Compare SmartOffice to Drake)

4. What % are financial and tax clients  (Compare SmartOffice to Drake)

5. What % have we done consulting on Medicare Supplements  (SmartOffice but not accurate)

6. Tax clients that have financial assets elsewhere (Drake but not good data)

7. What is the total annuity business we have? (SmartOffice but not accurate)

We understand that not all questions can be answered. We would like an estimate in hours and we may want to convert this project to a fixed price one based on your estimates. We would also like to know the forma in which you will deliver the results.

I am building a Financial Services company for Microbusinesses, with the first offering an online factoring service that will advance money against accounts receivables. 

I need business and technical help from someone with experience in serving the financial needs of companies, including freelancers, with 1-5 employees. 

Ideal expert will have experience with lending, credit scoring, risk management, etc. Previous experience with companies such as Kabbage, Lending Club, Avant, a plus. Expert will contribute to overall strategy as well as analysis of available data sources and how they should be used in decision-making.   

Expert will play an advisory role on this project. Role could be expanded in future to help build out the platform.  

Would like to get started as soon as possible. 

We're looking to develop a methodology to rank a few hundred domains based on their propensity to incur an algorithmic penalty in Google. A couple years ago, Google got more aggressive with it's determination of "unnatural links" and we're looking to take a list of sites and rank them. This is possibly a simple statistical problem, but we have a number of questions to ensure that we're computing these rankings based on something that is sound and will stand up to the scrutiny of the persnickety SEO community.

POTENTIAL DATA INPUTS:

As inputs for this computation, we have a number of tools at our disposal. Each of these tools is independent of each other, indexing their own subset of the web and computing metrics that are loosely based on the original PageRank methodology to some degree. What follows is a non-exhaustive list of those available metrics, highlighting only the ones that we believe would be useful in determining this rank score. 

Moz (formerly SEOmoz) - Moz invented the "open" link index and as such their metrics are largely the industry standard. They don't update as often as some of the other tools, but they offer very precise data points. See the following metrics, with more detail at their accompanying links.

• Domain Authority - https://moz.com/learn/seo/domain-authority
• MozRank - https://moz.com/learn/seo/mozrank
• MozTrust - https://moz.com/learn/seo/moztrust
• Spam Score - https://moz.com/blog/spam-score-mozs-new-metric-to-measure-penalization-risk
• Number of Linking Root Domains
• Total Number of Links
• Number of NoFollow Links
• Number of DoFollow Links

Majestic (formerly MajesticSEO) - Moz's main competitor with what many believe is the biggest index of links.

• Citation Flow - https://majestic.com/support/glossary#CitationFlow
• Trust Flow - https://majestic.com/support/glossary#TrustFlow
• Topical Trust Flow - https://majestic.com/support/glossary#TopicalTrustFlow
• Number of External Backlinks
• Number of Referring Domains
• Number of Referring IPs
• Number of Referring Subnets

Quality of Link Velocity - This is a qualitative binary metric that we would determine based on whether we believe they are building too many links too fast. 

Ahrefs - This is the later entrant into the link index game. They have one of the best UIs, update the quickest and arguably have the biggest index. 

• Number of backlinks
• Number of Referring Domains
• Domain Rating - https://blog.ahrefs.com/new-algorithm-for-domain-rank/
• Number of NoFollow
• Number of Dofollow
• Quality of Link Velocity - This is a qualitative binary metric that we would determine based on whether we believe they are building too many links too fast.

SearchMetrics Essentials - This is a platform that crawls a number of domains and collects rankings and extracts other features from the site. This will give us an indication of the trend of visibility. There's also a visibility score. We'd identify this qualitatively as to whether the domain is trending upward or downward.

CognitiveSEO - This tool is pretty in-depth with regard to link analysis. It offers link visualization and a specific computation of the percent of links that are algorithmically determined as unnatural based on its own machine learning algo. The user (us) has to determine which anchor text is considered branded versus miscellaneous vs. commercial. From those inpuots, the tool makes a determination.

The metrics returned are:

• Percentage of unnatural links
• Number of unnatural links
• Percentage of suspect links
• Number of suspect links
• Percentage of OK links
• Number of OK links
• Number of Links Analyzed
• Average authority
• avg of Good authority
• avg of low authority
• avg of high authority

Here's more info on how their unnatural link tool works. http://cognitiveseo.com/blog/3068/automatic-unnatural-link-detection/

We can provide all of this data for a subset of the list in order to support development of the methodology in CSV format.

Our main question around the usage of the CognitiveSEO is, how should we determine statistical significance? As it stands, the tool pulls a relatively low number of links per site. However, they just made an upgrade that allows us to pull a bigger sample size. Should we be computing stat based on the standard formula or will a certain small percentage of links per domain be enough? 

OUTPUTS OF THIS ENGAGEMENT

We're looking for specific direction there as well as a formula for ranking based on some combination of the above metrics. We'd also like an written explanation describing the methodology that can be inserted into the resulting whitepaper. We're happy to give you credit for that methodology once we publish.

We're of course open to any insights as to what else we might want to consider based on the expertise of whomever we end up working with.

This project should require 10 hours of work.