Exp: 6 - 12 years
CTC: 45 - 55 LPA
- Talents from Tier 1 Tech Schools Only / Should be from Product Dev Firms
- Degree in a relevant study area is required (e.g. computer science, mathematics, operations research, statistics, business analytics, engineering
- Build competitive real time Ad-fraud detection systems for the digital-ad ecosystem.
- Building competitive bidding systems to identify robust and efficient ad-campaign utilisation.
- Build custom demographic characteristics based on media consumption habits and behaviour embeddings
- Building Automated Anomaly Detection Solutions across businesses.
- Deep Learning Products on Video and Image Processing.
- Good understanding of model validation processes and optimizations.
- Good applied statistics skills, such as distributions, statistical testing, regression, etc.
- Strong hands on scripting and programming experience in Python
- Great analytical skills and excellent hands on experience in Machine Learning techniques such as Decision Trees (Random Forest), SVM, GBMs, Topic modelling, Regression (Linear & Logistic) and Deep Learning algorithms like CNN, RNN and LSTMs.
- Ability and willingness to go through research papers and implement ideas/algorithms for product/platform development.
- Fluency using different machine learning techniques and algorithms, ideally using ML , Deep Learning frameworks & Statistical libraries such as Scikit-learn, Tensorflow, Keras, Pytorch etc.
- Work with engineers to translate models into
- ralable high-performance engineering systems
- Actively involve in envisioning and design discussions of new A1 based assets/platforms with product stakeholders
- Exposure to Big Data technologies Hadoop, Hive, MapReduce, SparkML
The candidate would develop large scale and high-impact data science products to understand and derive insights from the billions of data points collected on our platform.
Some of the areas that the Data Science team would be involved in:
Desired Skills and Experience
Machine Learning, Big Data, deep learning, Image Processing, Regression, Recommender Systems, Statistics, Artificial Intelligence