Senior Data Scientist - Deep/Machine Learning (4-8 yrs)

Job Description :

Responsibilities :

- Execute the full Machine Learning lifecycle feature engineering, training, validation, scaling, deployment, scoring, monitoring, and feedback loop.

- Apply Supervised and Unsupervised Machine Learning techniques, including, Forecasting, Anomaly Detection, and Pattern Detection.

- Utilize a range of algorithms such as Decision Trees, Time Series Analysis, Bagging and Boosting algorithms, Neural Networks, and Deep Learning.

- Ensure compliance with regulatory standards for data handling and classification.

- Proficiency in SQL and relational databases, along with experience in Big Data technologies

(Spark/Hadoop) and Cloud technologies.

- Conduct statistical and machine learning methods like A/B testing, experiment design, causal inference, and recommendation systems.

- Define and develop Key Performance Indicators (KPIs) and create reports to measure the impact of tests, content releases, and product improvements.

Requirements :

- Preferred PhD or Master's degree in engineering, technology, computer science, machine learning, operations research, statistics, mathematics, or equivalent quantitative field. Master's degree in Statistics, Economics, or a quantitative discipline, or equivalent practical experience is an added advantage.

- 4 - 7 years of experience as a statistician, or data scientist, including statistical data analysis with linear models, multivariate analysis, stochastic models, and sampling methods.

- Sound knowledge of ETL practices, preferably using Spark in Data Bricks or similar cloud big data technologies like Microsoft Azure or AWS.

- Experience with traditional and modern statistical techniques such as Regression, Support Vector Machines, Regularization, Boosting, Random Forests, and Ensemble Methods.

- Proficiency in analytical programming languages (Python or R) and SQL, along with experience in statistical software like R, Python, MATLAB, and pandas.

- Strong understanding of machine learning models, feature engineering, validation, scaling, deployment, and monitoring.

- Ability to work independently and in a team environment.

- Strong problem-solving skills and attention to detail.

- Excellent communication and presentation skills.

- Proven track record of delivering high-quality data science solutions.

- Experience with programming languages for ETL operations and data analysis, including

Python, Java, C++, and R.