Senior Data Scientist in Predictive Analytics and Time Series Modelling
We are seeking a highly skilled and experienced Senior Data Scientist with expertise in Predictive Analytics and Time Series Modelling. As a Senior Data Scientist, you will play a key role in scaling our AI and ML products and solutions to analyze large and complex datasets to uncover valuable insights. You will be responsible for developing and implementing predictive models using statistical and machine-learning techniques, and evaluating model performance to make recommendations for improvement.
Responsibilities:
Scale AI and ML Products: Lead efforts to scale our AI and ML products and solutions, enabling analysis of large and complex datasets to derive actionable insights.
Predictive Models: Design and implement predictive models in our products and solutions using advanced statistical and machine-learning techniques.
Model Evaluation: Evaluate the performance of predictive models and provide recommendations for enhancing their accuracy and effectiveness.
Stakeholder Communication: Effectively communicate the results of analysis and predictive models to stakeholders in a clear and actionable manner.
Cross-functional Collaboration: Collaborate with cross-functional teams to deliver actionable insights and predictive models that drive business value.
Stay Current with Industry Trends: Keep abreast of the latest industry trends and emerging technologies in predictive analytics to continuously improve our solutions.
Mentorship: Mentor junior data scientists and share your expertise to foster their growth and development.
Requirements:
Bachelors or Masters Degree: Possess a Bachelors or Masters degree in a related field (e.g., Statistics, Mathematics, Computer Science, etc.).
AI and ML Expertise: Demonstrate strong experience as an AI and ML-based statistician in the IoT domain.
Predictive Analytics and Time Series Modelling: Showcase a solid background in predictive analytics and time series modelling, with a track record of designing and implementing scalable AI products and solutions for predictive modelling.
Statistical and Machine Learning Techniques: Have in-depth knowledge of statistical and machine learning techniques relevant to predictive analytics.
Programming Skills: Proficiency in programming languages such as Python and PySpark is a must.
Signal Processing Techniques: Experience in Signal Processing Techniques like Fourier transforms, Wavelet transforms, and Kalman filters to extract relevant information from sensor signals and enhance predictive model accuracy.
Product Engineering: Possess prior experience in Product Engineering.
Fast-paced Environment: Be able to work efficiently in a fast-paced and dynamic environment.
Desired Exposure:
- Data Structure and Algorithms
- Linear Algebra, Probability Theory, and Optimization
- Methods for Function Approximation
- Classification Models (Probabilistic Models, Discriminative Learning, Non-Metric Methods, Ensemble Methods)
- Dimensionality Reduction (Principal Component Analysis, Fisher Discriminant Analysis, Multiple Discriminant Analysis)
- Pattern Clustering
- IIoT (Industrial Internet of Things)
- Domain Knowledge: Industry-specific domain knowledge in Predictive Analytics, Vision Analytics
- International Journal Publications
Requirements
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