As a Senior Data Scientist, you are expected to break the business problem into a data science problem with clear demarcation of what we have, what we need and what needs to be done to ensure the business problem is solved with an optimal analytical model.
1. Visualizing the scope of the project end to end
- Identify the expected outcome
- Figure out the data present and required for successful completion of the project
- Defining the scope and effort needed for project execution
- Devising monitoring frameworks
2. Stakeholder management
- You should be able to communicate with business stakeholders proactively to find out their pain points and propose a data backed solution to them
- Should be able to formulate the complete steps to execution of the project with clarity on timelines
- Should be valuable with data storytelling to ensure analytical models are easy to consume for different business functions
3. Data standardization and integrity efforts
- Ensuring we keep sanitizing our data sources and keep raising flags to functions who own these data sources for data issues
- Ensure checks and reporting engines are in place to pick up anomalies in data pipeline
4. Project management
- Ensure timely closure of projects on or before committed timelines
- Step by step monitoring of Projects from whiteboarding to deployment
- Ensure model monitoring and communication around unusual spikes (if any)
5. Mentoring
- Mentoring junior resources on their projects
- You must bring your industry experience into the project to ensure junior resources are able to correlate their work with business impact
1. SKILLS AND KNOWLEDGE
- Proficiency in Python/R
- Pyspark and cloud tech exposure is a plus
- Expertise in SQL querying
- Product and model deployment on cloud platforms like GCP/AWS
Educational Qualifications
Qualifications
- Qualifications preferred: PhD/Masters/Bachelors in Statistics, Engineering, Economics, Machine learning, Computer science, IT etc.
Work Experience
- Proven experience of 4+ year of relevant experience
- Other skills – Data Interpretation, Visualization, Programming
- Knowledge of underlying mathematical foundations of statistics, ML and analytics
- Experience in EDA, model development and validation
- Experience in building and evaluating algorithms and optimizing the ML models in production
- Experience in productionizing algorithms for real – time systems
- Experience in influencing product and sourcing strategies with data centric presentations
- Experiencing with statistics for statistical inferences, testing strategies, evaluating regression problems