Senior Manager, Data Science

What you'd actually do

1. Model Assessment and Validation Requires knowledge of model fit testing tuning and validation techniques eg Chi square ROC curve root mean square error etc Impact of variables and features on model performance To Identify the model evaluation metrics Apply best practice techniques for model testing and tuning to assess accuracy fit validity and robustness for multistage models and model ensembles
2. Data Visualization Requires knowledge of Visualization guidelines and best practices for complex data types Multiple data visualization tools for example Python R libraries GGplot Matplotlib Ploty Tableau PowerBI etc Advanced visualization techniques tools Multiple story plots and structures OABCDE Communication influencing technique Emotional intelligence To generate appropriate graphical representations of data and model outcomes Understand customer requirements to design appropriate data representation for multiple data sets Work with User Experience designers and User Interface engineers as required to build front end applications Present to and influence the team and business audience using the appropriate data visualization frameworks and conveys clear messages through business and stakeholder understanding Customize communication style based on stakeholder under guidance and leverages rational arguments Guide and mentor junior associates on story types structures and techniques based on context
3. Understanding Business Context Requires knowledge of Industry and environmental factors Common business vernacular Business practices across two or more domains such as product finance marketing sales technology business systems and human resources and indepth knowledge of related practices Directly relevant business metrics and business areas To Provide recommendations to business stakeholders to solve complex business issues Develop business cases for projects with a projected return on investment or cost savings Translate business requirements into projects activities and tasks and aligns to overall business strategy and develops domain specific artifact Serve as an interpreter and conduit to connect business needs with tangible solutions and results Identify and recommend relevant business insights pertaining to their area of work
4. Analytical Modeling Requires knowledge of feature relevance and selection Exploratory data analysis methods and techniques Advanced statistical methods and bestpractice advanced modelling techniques eg graphical models Bayesian inference basic level of NLP Vision neural networks SVM Random Forest etc Multivariate calculus Statistical models behind standard ML models Advanced excel techniques and Programming languages like RPython Basic classical optimization techniques eg NewtonRapson methods Gradient descent Numerical methods of optimization eg Linear Programming Integer Programming Quadratic Programming etc To select appropriate modeling techniques for complex problems with large scale multiple structured and unstructured data sets Select and develop variables and features iteratively based on model responses in collaboration with the business Conducts exploratory data analysis activities for example basic statistical analysis hypothesis testing statistical inferences on available data Identify dimensions and designs of experiments and create test and learn frameworks Interpret data to identify trends to go across future data sets Create continuous online model learning along with iterative model enhancements Develop newer techniques for example advanced machine learning algorithms auto ML by leveraging the latest trends in machine learning artificial intelligence to train algorithms to apply models to new data sets Guide the team on feature engineering experimentation and advanced modeling techniques to be used for complex problems with unstructured and multiple data sets for example streaming data raw text data
5. Model Deployment and Scaling Requires knowledge of impact of variables and features on model performance understanding of servers model formats to store models To deploy models to production Continuously log and track model behavior once it is deployed against the defined metrics Identify model parameters which may need modifications depending on scale of deployment

Skills

What the JD emphasized

• model fit testing tuning and validation techniques
• model evaluation metrics
• model testing and tuning
• multistage models and model ensembles
• data visualization tools
• model deployment
• model behavior
• advanced machine learning algorithms
• auto ML

Other signals

• model deployment
• model testing
• model validation
• model tuning
• data visualization
• analytical modeling
• machine learning algorithms