We are looking to to do ongoing (monthly) social tracking & monitoring of the artists (photographers) that we represent.

The project will be broken into two phases:

Phase 1: Collect data on social media, social following, and social influence of our client's artists. The ultimate goal is to analyze the data sources to determine an artist's reach vs. influence upon the fahion industry/community in regards to marketing, social, and trending backgrounds. This analysis will be used to promote the artist's reach along with his/her portfolio of work. The deliverable will be an intuitive dashboard to present to clients.  There will be 2 or 3 photopgraphers to start.  Key platforms are Instagram, Tumblr, Facebook, Twitter, and others.

Phase 2: Predict the success of advertising campaigns by analyzing numerous data sources to determine the ROI of an adversiting campaign. (Reach, Influence, Etc.)

In other words, provide the ability to predict the success of a particular advertising campaign based on brand, talent, external factors, and trends 

We are looking with someone with marketing, social and trending backgrounds.

We are looking to invest in one or more of the social monitoring platforms, and would like input.

We are looking for creativitiy and someone, who will utilize various data sources we may not be aware of. 

Deliverables for Monthly Reporting

Dashboard creation

Platform tool setup

Monthly reporting (if needed)

THE SCOPE OF THIS PROJECT HAS CHANGED AFTER ROADMAPPING. PLEASE REVIEW REQUIRMENTS AND EDIT ACCORDINGLY. 

Objective:

Seeking data scientist with strong programming capabilities to build a price optimization application that will predictively change pricing on self storage units ahead of seasonal demand periods based on historic sales data. The goal is to adjust prices to maximize revenue and maintain property-level occupancy through peak and trough demand seasons. To achieve our goal the candidate will need to build a prototype application that we can enhance as we learn and as additional data inputs are available.

Requirements:

·         Automate current data processing: Establish API data feeds to read/publish data with two or more external vendor systems, process this data and store in a database for use by price application.

·         Price optimization application: Automate existing excel-based pricing model and manual processes into a custom application that can be run weekly or daily to make price changes on self storage units.

·         Reporting: The application will need to integrate with an existing or new reporting system to track and measure success of the pricing application.

The data I collected:

·         Monthly unit-level sales activity and price points from 2011 – 2015.

·         Monthly unit-level competitor prices for past year.

Technology & Vendor APIs:

·         Majority of pricing work is currently performed in excel, but the intent is to move beyond this.

·         We have a local SQL database and IBM Cognos TM1 reporting infrastructure, but neither are currently utilized in pricing or reporting processes. We are open to technology stack recommendations.  

·         Vendor APIs are available, but need to be developed on our end with the following vendors (https://slc.centershift.com/store40/SWS.asmx), (http://www.stortrack.com/index.php/solutions/datastream).

Position Summary:

We are seeking an experienced and talented Biostatistician to provide statistical and strategic support related to clinical development plans, study designs, and analysis of data for analytical and clinical studies.

Principal Responsibilities and Duties:

Contribute to design of clinical trials with appropriate statistical methods and sample size to support regulatory submissions in the US and worldwide

Develop and execute data analysis plans, resulting in written reports with tables, listings and figures for regulatory submissions

Collaborates in the preparation and review of study protocols for clinical trials and analytical studies

Communicate project requirements for CRF design, database design and database cleanup to ensure the key study variables are suitable for analysis

Qualifications:

Formal Training/Education:

Master’s degree required, PhD preferred.

A minimum of 5 years relevant biostatistics experience

3-5 years medical diagnostic trials experience preferred

Experience and Required Skills:

Significant experience in analysis and reporting of study results using statistical software such as R, S-Plus, SAS or Stata

3-5 years biostatistics/data management and/or related work experience in in vitro diagnostic medical device or pharmaceutical development

Experience in 510(k) and PMA trial design and execution preferred

Design of Experiments (DOE) experience

Working knowledge of ICH guidelines, GCP, clinical research, clinical trial processes and related regulatory requirements and terminology

Detail-oriented, self-motivated, ability to work independently and in a team-oriented setting

Ability to effectively communicate verbally and in writing with project team members

 

Role will require travel and/or relocation for a 6 month period to Tuscon, Arizona. 

We are looking for a Quants/Financial engineer with relevant experience of analyzing and writing strategies using tick by tick data of various Stock exchanges and come out with market deployable strategies.

Strategies which can be deployed sucessfully in HFT environment are Statistical Arbitrage ,Pairs, Index Basket and Predictive Analytics are few areas we are looking. 

Selected candidate will be using One Tick Software as a Tool for analyzing tick by tick data which has plugins for C++,Python,R and Matlab. The delivered Strategies should  be back tested on the One Tick software for the robustness and optimized and should pass the muster for profitability and risk.

Preference will be given to candidates with strong academic base, ability to interpret academic journal, strong data research capability, and apply theoritical concepts to real life scenarios.

In your proposal, please indicate how many hours you will require to compete this project. 

We are on a mission to build the next generation of mobile game usage intelligence to help to identify the most valuable users.

Real iOS Gamer Activity Intelligence, Not Just Installs & Ranks 
Have your ever wanted to compare mobiles games usage and base your decisions on how players really play and not only on how games are ranking in the app stores?
With no SDK, Whally tracks anonymously 400+ million players posting daily more than
80+ million scores within 20 game categories on 190,000+ iOS mobile games.

Whally.com has released 2KPI's to date : 
- Whally Index show a iOS game index based on daily player activity for games that offers public leaderboards
- Whally Overlap shows where players are across mobile games, analysed daily

• The problem that you are trying to solve 

Whally is tracking a subset of best iOS games around 25% of all 150000 best games. All data collected should provide us with enough material to extrapolate results for all 150 000 games. The goal is to provide an estimate of active scorers for every game in AppStore.

• The kind of expertise you require 

Whally needs expertise in data development along with notions in mathematics to build a interpolation and extrapolation tool to estimate scorers activity for all iOS games. The tool needs to take into consideration app ranks, number of active players and computes an estimate for missing data points along with the error margin associated.

• The data sources at your disposal and their formats (Pricing data in CSV format, etc.) 

All the data is crawled from the sources into a Cassandra database. A few tables cover the data sources, including the list of applications with their associated ranks per day (segmented by game sub-category (trivia, action, sport, etc), model (free, paid, grossing) and countries), and also the associated active players per application and per day.

• What is your current technology stack? (D3.js, Ruby on Rails, Hadoop, etc.) 

Data storage : Cassandra cluster
Data processing : Apache Spark (jobs currently written in scala, but python jobs are ok), Pandas for python/scikit-learn

• What is the deliverable? (Algorithm, dashboard, web application, advisory service, etc.) 

Deliverable will be Apache Spark jobs in Scala/python or Pandas/scikit-learn Python code that Whally can deploy to ensure daily processing.

• Does the deliverable need to be deployed in the cloud or your infrastructure? 

No, Whally will cover this part.

• Attach a sample data file, if you can 

Not really applicable, but schema of the database can be provided, though.

Expand existing SSIS package functionality so it can continue to update the clean “gold standard” end of day database daily, in a forward-looking fashion. The package will connect to the four data sources (tblHistoricalData, Google, Yahoo, XIgnite) and construct a new record for each US ticker symbol each day. Also, the SSIS package will need to be adjusted so we can install it within our SQL Server 2012 environment.

 Note: this project is being awarded to the same expert who performed the first phase.

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We have a trident drpc topology with heavy parallelism to perform event based queries. Our drpc topolgy generally runs with a latency of 8 to 12 seconds whereas we'd expect the latency to be 1 - 2 seconds. 

During testing, we have observed that our execute latency for each function is under 10 to 15ms but the process latency is higher (in the order of 100s to 1000s of ms). The topology capacity is under 0.5. We've tried to improve the process latency by increasing the executor queue buffer size and adjusting other parameters without noticeable difference.  Attached is a screenshot of the topology running.

The expert must have Storm Trident expertise to help us scale and tune our Trident topology.

We are a Boston-based education technolgy fast emerging company delivering highly scalable, turnkey, cloud based learning data management and analytics enabling capabilities across the online education markets.

We are looking for a data scientist to work on learning analytics.  Your job will be to work on building a series of models and driving insights via visualizations and other API based delivery. 

The current and future digital world of online education is increasingly more federated across a wide spectrum of platforms and apps each with their own siloed data from multiple providers that are integrated to comprise online curriculums.  This creates many rich and interesting learning experiences but presents challenges with regard to providing a more holistic and unfied learning analytics solution.  The Intellify LIaaS cloud based platform of learning data management and analytics enabling services resolves these challenges very effectively with a comprehensive end-to-end solution for real-time collection, storage, access and advanced analytics.

We are seeking to augment our team with someone with strong data science skills and preferred domain knowledge and/or experience within learning science to leverage our platform to build advanced models for advanced correlative, predictive outcomes, at risk detection, and efficacy oriented analytics insights that can then be delivered via visualizaions, streams, alerts and / or other APIs.

The data scientist would need to spend some time with us in-person in Boston in the beginning and then this can be a remote position.

We have two distinct projects with aggressive deadlines.  These projects collectively will serve as a proof of concept for a research platform that we would like to build.  We intend to hire two data scientists, one for each project.

Project 1: Academic Network Formation Detection

Objectives

1. Discover interesting emerging technologies by clustering related academic publications and researchers, and be able to track their development over time. (a). We are particularly interested at discovering points in time where networks emerge (b). “Interesting” could be defined as when similar topics begin to see increases in publications at multiple institutions by many different researchers
2. In the process of doing the above network analysis, build an underlying data asset (ideally in a relational database) of publications, people, and institutions, and time-stamped affiliations.

Project Description

The finished project will include scripts/software to scrape/pull the dataset from one or more sources (e.g. ResearchGate) and

• Create a table of publications (title, date, abstract)
• Create a table of authors (associated with the publication)
• Create a table of institutions (associated with authors at a point in time based on a publication)
• Classify the publications based on similarity of text in abstracts (or authorship or other

You will also deliver the actual database/files produced by the above scripts.

We are open to recommendations of techniques, approaches, and strategy based on the expertise and experience of the expert.

We have no particular requirement around tools, programming language, or methodology. We will ultimately need to refresh our dataset at least monthly; however our immediate need is to have historical data for a proof of concept.

We expect the output to eventually be imported into a relational database, and merged/deduped with people, institutions, and relationships from other data sources. While we currently don’t have a full working taxonomy, we also expect to combine the categorization scheme extracted from this data source with other data sources (e.g. company profiles/descriptions, patents, press releases).

Boundaries

To narrow the universe of publications and people for this dataset, we are only interested in publications, people, and institutions in any STEM discipline, within the date range of 2000-present.

Project 2: Entity extraction and classification from patent filing databases

Objectives

1. Discover interesting emerging technologies by clustering related companies and people by classifying them by topic/technology based on their patent filings over time
2. We are particularly interested in trends in new cluster formation and predicting growth in certain technology areas

Project Description

The finished project will include:

• Scripts/software to build the classifications from Thomson Innovation or other patent sources and
• A database or files containing the data on the classifications/clusters, and appropriate visualizations to show growth of clusters over time

We are open to recommendations of techniques, approaches, and strategy based on the expertise and experience of the expert.

We have no particular requirement around tools, programming language, or methodology.

We expect the output to eventually be imported into a relational database, and merged/deduped with people, institutions, and relationships from other data sources. While we currently don’t have a full working taxonomy, we also expect to combine the categorization scheme extracted from this data source with other data sources (e.g. company profiles/descriptions, patents, press releases).

Boundaries

To narrow the universe of patents for this dataset, we are only interested in publications, people, and institutions in any STEM discipline, within the date range of 2000-present.

Proposal

Please specify which of the two projects you are most interested in and how much time you can dedicate on a weekly basis.  An estimate of how long it would take to create a proof of concept would be very helpful.  We would also like to understand your specific methodology to tackle the challenges described above.