Lead / Senior Data Scientist

Job Description

We are looking for a senior data scientist in the PayU intelligence team who will be primarily responsible for modeling

complex problems, discovering insights, and identifying opportunities through the use of statistical, algorithmic, mining,

and visualization techniques. Your primary focus will be to propose innovative ways utilizing graph databases and analytics

to look at the problems by applying data mining techniques, doing statistical analysis, validating your findings using an

experimental and iterative approach, and building high-quality prediction systems integrated with our services. You will

need strong business understanding, analytical and problem-solving skills, and programming knowledge.

Responsibilities

Design experiments, test hypotheses, and build models utilizing the traditional datasets and graph data.

Apply advanced statistical and predictive modeling techniques to build, maintain, and improve on multiple real-time

decision systems.

Identify what data is available and relevant, including internal and external data sources, leveraging new data collection

processes such as geo-location or social media

Utilize patterns and variations in the volume, speed and other characteristics of data for predictive analysis.

Define the preprocessing or feature engineering to be done on a given dataset, data augmentation pipelines, training

models and tuning their hyperparameters, analyzing the errors of the model and designing strategies to overcome them

Selecting features, building and optimizing classifiers using machine learning techniques

Extending company’s data with third party sources of information when needed

Creating automated anomaly detection systems and constant tracking of its performance

Skills and Qualifications

Bachelors in mathematics, statistics or computer science or a related field; Masters or PHD degree preferred.

5+ years of relevant quantitative and qualitative research and analytics experience.

Extensive knowledge of statistical techniques.

Ability to come up with solutions to loosely defined business problems by leveraging pattern detection over potentially

large datasets.

Proficiency in statistical analysis, quantitative analytics, forecasting/predictive analytics, multivariate testing, and

optimization algorithms.

Proficient in deep learning (CNN, RNN, LSTM, attention models, etc.), machine learning (SVM, GLM, boosting, random

forest), graph models and reinforcement learning 

Experience with open source tools for deep learning and machine learning technology such as Keras, tensorflow, pytorch,

scikit-learn, pandas, etc.

Strong programming skills (Hadoop MapReduce or other big data frameworks, Java, Python), statistical modeling (R,

Python, SAS), query languages such as SQL, Hive, Pig

Familiarity with basic principles of distributed computing and distributed databases.

Demonstrable ability to quickly understand new concepts - all the way down to the theorems - and to come out with

original solutions to mathematical issues.