Lead / Senior Data Scientist

PayU; India; On-site; INR 1,800,000 - 3,200,000; Full time

Job summary

PayU invites a Senior Data Scientist to join the intelligence team to model complex problems, discover insights, and build predictive systems. You will apply statistical, mining, and visualization techniques with a focus on graph databases and graph analytics to drive business opportunities and product enhancements.

The role demands strong business sense, analytical thinking, and programming proficiency in Python/Java and big data frameworks, enabling scalable, real-time decision systems

Qualifications

• Strong background in statistical techniques and data-driven problem solving.

Responsibilities

• Design experiments and build models using traditional and graph data.
• Apply predictive modeling to real-time decision systems.
• Identify relevant data sources and leverage new data collection methods.
• Use data patterns to forecast outcomes and optimize performance.
• Define preprocessing, feature engineering, and model training with hyperparameter tuning.
• Select and optimize classifiers using ML techniques.
• Incorporate external data sources as needed.
• Create automated anomaly detection and monitor performance.

Skills

Statistics

Data mining

Graph analytics

Deep learning

Machine learning

Python

SQL

Big data

Hadoop

Education

Bachelor's in mathematics/statistics/computer science

Master's or PhD preferred

Tools

Keras

TensorFlow

PyTorch

scikit-learn

Pandas

Job description

PAYU PAYMENTS PRIVATE LIMITED 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.