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Organic Seasonality Model on H2O Platform

Our business

We are building predictive models for cross channel marketing automation. Our clients use our services to understand the impact of their paid media activities on their business, and to plan the optimal future media activities based on this understanding.

For this purpose, we collect large amounts of relevant data, and build a variety of analytical models. The typical data collected include:

  • our client’s marketing activities, particularly paid media,
  • competitor’s marketing activities,
  • weather, 
  • calendar information; holidays, paydays, etc, 
  • macro-economic factors, such as consumer confidence, unemployment, etc. 

Our models predict a predefined KPI - usually some kind of sales parameter such as web store sales, new signups, store revenues, etc.

The models we are building have two major uses: 
- To predict the KPI based on a given paid media spend in the past or future.
- To recommend the optimal media allocation - i.e. the distribution of spend per paid media channel, and the temporal distribution over a period of time.

The Project

As part of our ongoing research into better marketing models, we want to implement a new approach to modeling which we believe will be more scalable, better performing and easier to maintain. The basic idea is to have several collaborating models, each serving a specific purpose.

With this project, we want to, as a proof of concept, build an "Organic Seasonality Model".

The Organic Seasonality Model is expected to isolate the organic development over time of a client's KPI. One way to achieve this is to remove all other causes of KPI fluctuation, such as the client's own media spend, competitor activity, random events, etc. Other approaches could work as well.

Based on standardized input data, which is the same across all our client, the model should produce a standardized output which can be used by other models or visualized in our client facing UI.

The output of the model is a time series with daily granularity which represents the natural seasonality of the client's KPI. The output should exhibit all seasonal effects, such as:

  • The overall trend of the client's KPI; increasing, decreasing or stable
  • The effect of day of week
  • The effect of the time of year
  • The effect of seasonal events (Christmas, Easter, etc).

The model should work for both generating insights, i.e. explaining what the organic past seasonality looked like, and for generating a predicted future seasonality for a given time period.

The latter should take known future seasonal impacts into consideration. For example, if the client's KPI is impacted by Easter, the predicted future seasonality should take the actual future dates of Easter into consideration.

We value engineering skills in the delivered code; a good structure, a reasonable level of software tests, and an attention to the fact that the model should work for a wide range of clients without change.

If the project is a success, we will extend the project to build other models as well.

The Technology Stack

We would like the project to be implemented in Python using our H2O platform which is running on our Hadoop Cluster.

The only restriction on which modeling methodology to use is that it should be supported by H2O. The Experts should choose the methodology believed to be the best.

The Team

The project team could consist of 3-4 Experfy Experts who will be working together with a project lead and our Data Engineering and Marketing Science teams.

We are looking for experts with strong capabilities within the area of modeling, data science and software engineering and prefer experts with experience from different modeling methods.

At times during the project we would like the project team to visit and work with people from our Marketing Science teams from Los Angeles and our Engineering teams in Barcelona, Spain and thus we prefer experts who are willing to travel.

Our preference is to have an expert for the occasional On-Site work in Barcelona, Spain, but we are open to considering Experts, who are not able to travel as well. 

We are interested in both individuals and already established teams.

Media and Advertising
Customer Acquisition Modeling

$100/hr - $150/hr

Starts Mar 06, 2017

2 Proposals Status: HIRING

Company small

Client: B***************

Posted: Feb 24, 2017

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Create Prototype Model for Life Insurance Sector

The attached requirement and methodology documents describe a problem in the life insurance space.  Our organization has already developed a model to fit the requirements and is looking to hire an expert from the Experfy community to develop a prototype model that we can use to validate our own.

Please review the attached documents and create a proposal to answer the following questions:

-Describe the approach you will use to develop the prototype model
-What assumptions will you make?
-What skills, experience or knowledge do you have that will help you with this task?
-How long both in terms of hours and timeline do you expect it will take to develop the prototype model?

Life Insurance

$150/hr - $300/hr

Starts Mar 06, 2017

4 Proposals Status: HIRING

Net 60

Company small

Client: C*******

Posted: Feb 17, 2017

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Data Strategy Advisor for Financial Services Company

We need a data scientist to advise us on our data strategy. This includes our current approach to data capture (including what pieces of information we should be tracking) as well as a strategy / timeline for implementing a new automated pricing and underwriting tool to improve our underwriting process.

We are a small, fast-growing merchant cash advance company. We need someone that has deep expertise in data science, a strong business acumen, and extensive experience in financial services - preferably with some experience with merchant cash advance companies. 

We are currently rolling out a new internal application that will track all aspects of the underwriting process in a SQL database. Prior to the application launch, most of our underwriting data is stored in PDF forms on our internal network.

The deliverable we are looking for is advice on:

  • What data we should be tracking with the launch of the new application
  • Approach to converting previous PDF data into a format that is able to be analyzed
  • Timeline / strategy for implementing an automated underwriting model based on this data
Financial Services
Fraud Identification and Prevention

$75/hr - $125/hr

Starts Feb 06, 2017

14 Proposals Status: CLOSED

Company small

Client: G****************

Posted: Feb 05, 2017

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Price Optimization of Condominium Units

We are a real estate developer that would like to develop a Proof of Concept (POC) predictive model to identify which factors influence the pricing and sales of a home and/or condominium units. The POC will utilize all available historical data for condominium projects. After the POC has demonstrated value with predictive power and an actionable implementation within the project, the scope may be expanded across a broad array of projects.

The POC will utilize the full history of condominium sales data, including (but not limited to):

  • Unit available for sale date
  • Unit list price
  • Unit sale date
  • Unit sale price
  • Agent incentive fee and characteristics
  • Unit characteristics
  • Building characteristics
  • Accessibility measures
  • Expected building transfer date
  • Advertising expenses
  • Brand
  • Project
  • Room Type
  • Room Size
  • Floor
  • Tower
  • Project Transfer Duration
  • Transfer Duration
  • Selling Duration
  • Price/Sqm
  • Price Increase/Decrease
  • Selling Discount
  • Transfer Discount
  • Resale Count
  • Resale Amount
  • Transfer Amount
  • SPA Amount
  • More to be determined

With the data organized into a structured format and validated for accuracy and completeness, the data scientist will build models to predict the optimal price for a unit given a collection of data points.

Restrictions, such as the sum of the revenue of all individual units must equal the set building revenue, will be incorporated as modeling options as required by the client.

For new model predictions, the heads of business units will enter building attributes, unit attributes, and all other required characteristics into a simple-to-use front end tool. The output generated will be a numeric suggested price. Additionally, work will be done to derive estimates for the effects of changes in each variable. Depending on the complexity desired of the model developed, and variation in the data across buildings, these by-variable impacts may or may not be attainable individually at a high accuracy level.

The statistical model will be developed in R, accompanied with a spreadsheet and/or PowerPoint deliverable that gives details on the functional form of model, examples of how historical sales patterns fit the function, and measures of model accuracy.

The user tool for the POC will be published in Excel or other front end alternative (Shiny app) and serve for testing purposes to evaluate the quality of results and overall value. Recommendations for the expansion of the tool beyond the POC will be given for future implementation.

The client will have full access to all code and tools used in this project, both for model development and model deployment.

Note: This project is being awarded to a data scientist which whom we have an existing relationship.

Real Estate
Machine Learning


Starts Feb 02, 2017

1 Proposal Status: IN PROGRESS

Net 30

Company small

Client: E*******

Posted: Jan 31, 2017

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Knn Algorithm Addition in Cluster App

Building a series of breakthrough visualizations for many analysis tasks on platform. You are seeking a qualitatively improved way to view clusters of information, compared to existing methods. Viewing data that naturally “clusters together” is of value in many application domains, including data formatted as surveys,transactions, and text. In preparation for this project, we collaborated on a UI sketch, which we have rendered in a mockup image below. - 

Project is to be awarded to Expert already engaged in client's project work. 

Professional Services
Marketing and Brand Management

$41,000 - $42,000

Starts Jan 28, 2017

1 Proposal Status: IN PROGRESS

Net 7

Company small

Client: V********

Posted: Jan 26, 2017

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Voice Analytics - Sentiment Analysis Predictive Model

The goal of this project is to take an existing sentiment analysis framework and pipeline and apply to to additional call center calls to determine it's business value and predictive power. Note - this is the next phase of a completed project and will be awarded to the same data scientist who worked on the earlier phase

Professional Services
Call Center Analytics
Consumer Experience


Starts Jan 19, 2017

1 Proposal Status: IN PROGRESS

Company small

Client: T****

Posted: Jan 18, 2017

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Training Data Generation for Ansible Build Time Prediction


We would like to build a continuous learning algorithm that will be able to predict execution times of Ansible builds (Playbooks) based on historical Ansible build data.  As a precursor to developing the algorithm we are seeking a technologist to develop the continuous learning environment using free versions of Ansible, Splunk and Elastic Search and to generate training data from which the algorithm can learn.


As part of your proposal please answer the following questions:

  • What cloud environment will you use to develop the continuous learning environment?  Please describe or diagram the system and provide an estimated cost (e.g. EC2 instance costs or Heroku dyno costs) for maintaining the environment for data generation.
  • Please provide an estimate of hours required to build and configure the environment. Please provide an estimate of hours required to generate the training data.
  • What will be your strategy/approach for configuring Playbooks based on the specified Galaxy Roles?  How will you ensure the generated data provides a variety of Playbook structures (singletons, clusters, single and multi-target builds) for optimal machine learning?
  • How do you plan to structure the resulting training data?
  • Please describe your knowledge of and past experience with the technologies required for this project.

Scope of Work

The selected consultant will be responsible for:

  • Setting up a cloud environment for data generation (included in this project posting) and continuous learning (for the future project posting).
  • Setting up and configuring at least one Ansible instance.
  • Setting up and configuring Splunk.
  • Installing and configuring the Ansible App for Splunk (used to import Ansible data in to Splunk).
  • Installing and configuring Elastic Search to access the Ansible data within Splunk.
  • Setting up multiple hosts upon which Ansiblebuilds can be executed.
  • Configuring a selected set of publicly available Ansible Roles (from in to both singleton (single-Role) and cluster (multiple-Roles) Ansible Playbooks for the purpose of data generation.
  • Developing a script to execute the resulting Ansible playbooks against single and multiple hosts in order to generate approximately 2,000 rows of test data.
  • Provide a method for extracting the training data for machine learning (extracted data must be in a flat-file format).

The primary outputs of this project are both the test data and the environment for generating additional test data, which can be accessed by the continuous learning environment.

The attached presentation provides additional details around the environment and data requirements and gives additional context to the broader project scope (beyond the environment and data generation scope of this first project).  Details relevant to the scope of this Experfy project posting have been highlighted in yellow in the presentation for clarification.

When submitting your proposal please include an executive summary which describes key elements and numbers for your approach.  In your proposal, when estimating cost for the environment, please declare some assumptions regarding number and size of hosts and indicate how much of the proposed cost is due to environmental costs.

After the executive summary, the rest of the proposal should address all questions listed in the proposal section above.  The proposal does not need to explicitly be in question and answer format, however it can be.  The important thing is that all questions are clearly answered or, if they cannot be answered, then an explanation is given as to why.

Finally, please make sure all proposals are client-ready.

Application Deployment
System Provisioning & Configuration

$13,000 - $20,000

Starts Jan 28, 2017

8 Proposals Status: IN PROGRESS

Net 60

Company small

Client: C*******

Posted: Jan 12, 2017

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Data Scientist for Exploratory Analysis

This project will be awarded to the same data scientist as before. The details have already been discussed with the expert.

Consumer Goods and Retail

$100/hr - $150/hr

Starts Jan 18, 2017

3 Proposals Status: IN PROGRESS

Net 30

Company small

Client: M***

Posted: Jan 09, 2017

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Machine Learning pipelines for optimizing online marketing performance

We would like to create ML pipelines to improve the conversion performance (leads and sales) of the ads we manage on adwords, facebook ads, instagram, twitter ads.

We're looking for a long-term engagement with someone who ideally has some experience with applied ML in digital advertising.

About us

We're a digital advertising management company for SMBs. We launched in April 2016 and currently have ~150 active customers.


Overall, we're trying to improve the conversion performance of our customer's campaigns in an automed way. We believe in order to do this, we need to start by using ML pipelines to output suggested values for % of budget being allocated to the different channels (see above). We also believe there are other pieces to this puzzle but we want to start with the channel allocation suggestions and then move on from there.

We already have a team of developers that will performing any of the devops needed for this project.

So we're looking for a someone to help us do the following:

  • develop models in R or python using past experience and our data
  • help us develop the proper techniques to utilize the model pipelines

We plan on using AzureML to construct our pipelines/APIs. You do not need to know AzureML, you can pick it up along the way as we work together to implement the pipeline(s) you construct.


The data will be advertising performance data from adwords, facebook ads, instagram, twitter ads as well as web site and conversion analytics data.

This data will be ETL'd by us and made available to you in DBs to conduct your work.

However, ideally you have a method to access the APIs directly (ex. Pentaho) as well while performing your function to streamline the workflow (example you need access to something that we're not currently capturing from the APIs)... so that would be ideal, not required.

Online Advertising
Machine Learning
Market Segmentation and Targeting

$100/hr - $200/hr

Starts Jan 19, 2017

19 Proposals Status: IN PROGRESS

Company small

Client: A*****

Posted: Jan 08, 2017

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Proof of concept for a Web Page Classifier that identifies reader intent


Taboola is widely recognized as the world’s leading content discovery platform, reaching 1B unique visitors and serving over 360 billion recommendations every month. Recent ComScore data shows that Taboola is second only to Facebook in terms of reach (

Publishers, marketers, and agencies leverage Taboola to retain users on their site, monetize their traffic and distribute their content to drive high quality audiences. Publishers using Taboola include USA Today, NYTimes, TMZ,, BusinessInsider, CafeMom,, Fox Television,, Examiner, and many more.

Taboola's operation is vast with ~2,000 servers in 6 data centers processing big data about users and user behavior, content, pages etc..



The premise behind this project is that web pages can be used to identify a specific reader intent.
For example, people who read about a store’s opening hours have an intention to visit that store or people that read about “how to write a great CV” probably intend to seek employment.


What we are looking for:

Our goal is to have a reproducible methodology for building a web page classifiers for identifying specific user intents.  

Given a specific user intent, we would like to build a binary classifier that determines whether the reader has the specific intent, and would like to be able to reproduce this methodology with different intents.

Once operational, the classifier should run efficiently and be able to scale into classifying millions of web pages in a short amount of time.


Project scope:

The project deliverables should be a working classifier which will serve as a proof of concept for a re-usable methodology for creating such classifiers.

In addition the project should include ample documentation describing the general methodology used so it can be recreated for additional intents.  

We will decide as initial for the initial proof of concept with the selected candidate  


Your proposal:

Your proposal should outline your approach in general terms, which algorithms you intend to use, which features would you extract from each url and how, how would you determine a truth set for the classifier, how would you measure the correctness and/or other KPIs.

We will share additional information with the expert and define the approach and scope in detail with the relevant expert.


(Image provided by Mimooh under the Creative Commons Attribution-Share Alike 3.0 Unported License -

Consumer Goods and Retail
Financial Services

$10,000 - $15,000

Starts Jan 15, 2017

12 Proposals Status: HIRING

Net 30

Company small

Client: T*******

Posted: Jan 08, 2017


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