Key Responsibilities
  • Work with stakeholders throughout the organization to identify opportunities for leveraging company data to drive business solutions.

  • Select features, building and optimizing classifiers using machine learning techniques

  • Mine and analyze data from client databases to drive optimization and improvement of product development, marketing techniques and business strategies.

  • Assess the effectiveness and accuracy of new data sources and data gathering techniques.

  • Processing, clean, and verifying the integrity of data used for analysis

  • Perform ad-hoc analysis and presenting results in a clear manner

Required Qualifications
  • Experience in one or more of the following programming languages: Python, R, MATLAB, Julia

  • Experience in exploratory data analysis

  • Experience in data visualization using one or more of the following packages/tools: seaborn, matplotlib, plotly, ggplot, Tableau

  • Knowledge of machine learning techniques and algorithms, such as K-nearest neighbors, naive bayes, support vector machines, random forest, logistic regression, etc.

  • Excellent understanding of machine learning concepts such as overfitting and underfitting, the difference between bias and variance, generalization capability of the prediction model to unseen data, feature engineering, etc.

  • Excellent written and verbal communication skills for coordinating across teams

  • A drive to learn and master new technologies and techniques

Bonus Points
  • Data engineering experience;  e.g. SQL, Hadoop, Spark, cloud computing

  • Competitive programming experience (e.g. ACM, Topcoder, Code Forces, etc.)

  • Experience participating in machine learning competitions (e.g. Kaggle, Hacker Earth, etc.)

  • Strong statistics background

  • An up-to-date portfolio (on GitHub?) showing your experience in all of the above!

(Please specify in your email which position your applying for)




Digital Marketing & Advertising

Distribution & Logistics



Real Estate



Financial Services




By Use Case

Credit Scoring

Demand Forecasting

Customer Segmentation

Warehouse Location Optimization

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