Global Lead of Data

  • Proficiency on Advanced Statistics Modeling techniques, regression techniques, e.g. logistic regression, supervised learning, decision trees, random forests, etc.
  • Expert on statistical computing packages, SPSS, SAS, etc.
  • Proficient on visualization libraries and softwares such as Plotly, Matplotlib, Ggplot, Tableau (knowledge of NodeXL and PowerBI is a plus)
  • Full capability and experience on building mathematical models for descriptive, predictive and prescriptive solutions.
  • Proficiency on ML techniques, i.e. linear and non-linear regression, classification or regression algorithms such as SVM, LR, DNN, CNN and RNN (knowledge of Lgbm or Catboost is a plus)
  • Unsupervised learning: clustering, dimensionality reduction, etc.
  • High proficiency using libraries as Tensorflow, Keras, Numpy, Scipy, Pandas, Glnet, Nnet, Gbm, Mboost, etc
  • Scripting Languages: Scala, Python, R.
  • DataBase languages: SQL and No-SQL storing systems (e.g. MySQL, PostgreSQL, MongoDB, Redis or Cassandra)
  • Advanced experience in working with APIs like REST
  • Experience in configuration and usage of both virtual and dedicated machines provided by services as AWS and MS Azure (knowledge of cloud computing is a plus)

Cultural Fit

  • Ease at uncertain fast-paced environments.
  • Honesty.
  • Relentlessly.
  • Efficiency.
  • Attention to detail.
  • Analytical skills.
  • Organization and planning.
  • Fast Learning.
  • Creativity and Innovation.
  • Proactivity.
  • Goal driven.
  • Enthusiasm.
  • High Standards.
  • Communication Skills.
  • Teamwork.
  • Challenge willing.

Findasense is a fast-growing company. It has developed and deployed marketing and consumer interaction solutions to more than 40 markets over the last 4 years. Since 2011, our team has doubled its size every year, and now comprises more than 250 full-time professionals. Findasense is fully aimed and committed to creating an outstanding, global and networked organization, with innovation, creativity and smart client service practices at the heart of all operations and capabilities. We love humble, talented, and ambitious people with strong ‘can-do’ and ‘start-up’ attitude.


To create a data-driven culture and ecosystem within company, which will serve to develop profitable models that make a positive impact in our clients, people, stakeholders, and most importantly, for the world.

Main responsibilities:

  1. Leading a lean and high-performance global data science team.
    1. Defining the data products and analytics roadmap, its needed outcomes, competencies and roles for an extremely efficient data science team.
    2. Defining the upskilling strategy for the global data science team.
    3. Executing deep research projects either for clients or internal needs.
    4. Defining guidelines to data treatment processes within all company.
    5. Mentoring team members and partners.
  2. Building Descriptive, Prescriptive, Predictive, and Cognitive Analytics system::
    1. Designing and building out a predictive-modeling engine for rapid development of robust predictive models using varied data sources - demographic, behavioral, weblogs, payment data and social media.
    2. Developing data science capabilities for multi-channel Digital Marketing (e.g. email, Facebook, Twitter, Instagram, etc), and any other Marketing Data Point as required.
    3. Designing digital media monitoring algorithms (Sentiment analysis, Influencer analysis, Topic modeling and trend spotting) and scale them up towards real-time.
    4. Designing and developing algorithms and statistical models applied to our research marketing methodologies.
  3. Designing and developing a text and visual mining engine (either in-house or blended solutions with 3rd parties):
    1. Building proprietary NLP libraries for semantic analysis on structured and unstructured text data formats and sources.
    2. Building out a large-scale multi-class classification system for analyzing customer behavior along her consumer journeys.
  4. Designing and developing data-driven and transactional marketing methodologies.
    1. Contributing along with other practice leaders in our open organization to develop marketing methodologies fed by data streams, and data products that outperform in market.

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