DATA SCIENCE & ENGINEERING. ART
The data science team combines coding and math’s.
As a result you will take data driven decisions which will impact all parts of your business and deliver real growth.
WHAT WE DO
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Data Science
Our data science team brings their programming knowledge and mathematical skills coupled with domain experience to solve business problems.
They delve in artificial intelligence, machine learning and other supervised or unsupervised models depending on the business problem.
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Data Engineering
Our engineering team provides the data framework that enables our scientists and analysts to solve business problems.
We have made significant progress in this are over the past 12 months, investing in the analytics server and the new MarTech environment.
Check out some of our Projects below:
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Segmentation
Segments are used to divide the target market into manageable groups to offer a tailored experience of our products and services
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Marketing ROI
We evaluate the marketing touchpoints a consumer encounters on their path to conversion and link spend to revenue
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Lifetime Value
Projecting lifetime value of a client is important for acquisition, retention sales and value management teams.
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Propensity Models
Be it lead scoring for new acquisition or churn models, propensity models are key to provide a targeted service to a Retail client base
Meet the Team
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Anirban Banerjee
VP OF COMMERCIAL INSIGHTS AND ANALYTICS
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Elettra Damaggio
DIRECTOR OF DATA SCIENCE & ENGINEERING
Doing anything that Anirban doesn’t want to do.
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Sumeyra Karaca
DATA SCIENTIST
Working on NLP models. Projects: Text to Analytics.
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Agata Plewa
DATA SCIENTIST
Working on Customer Behavior Forecasting. Projects: Life time value, Personas, Churn Prediction
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Raina Li
DATA ENGINEER
Supporting the team into migrating in the new Martech Platform
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Chitra Pun
DATA ENGINEER
Supporting the team into migrating in the new Martech Platform
“The analytics team help us identify various commercial opportunities and we are excited to see what they have in store for us in 2022!”
— Sixto Alonso, VP Americas, Retail
Core competencies
Machine Learning
Using supervised and unsupervised machine learning techniques to solve business problems. Examples of what we used so far: K-means clustering, Neural Networks and XGBOOST random forests.
Data Integration / Engineering
Building ETL pipelines to collect, transform and process data from our various data sources in cloud and on premises, supporting commercial reporting and ML modules. The Data Engineering team also builds two-way connectors to make data points available where is most useful for the business.
Languages / Platforms
We code in: Python, SQL, Big Query SQL
We use: PowerBI, AirFlow, DAX, Google Cloud, sklearn and scikit learn, REST API.
Automation
Data automation for end to end reporting via Airflow (task scheduler).
We can develop automated process for Data treatment and consumption.