Principal Data Scientist, AI/ML

As a Lead Data Scientist with 8-12 years of experience, you will be responsible for the following:

• *Role Overview:**

You will lead a team of Data Engineers, Analysts, and Data Scientists to execute various tasks including connecting with internal/external points of contact to understand business requirements, creating project plans and sprints for milestones, optimizing clusters for Data Science workflows, creating data pipelines, conducting Exploratory Data Analysis (EDA) and Feature Engineering, defining performance metrics, researching existing solutions, running machine learning (ML) algorithms, creating optimized data models, developing light applications and scenario builders, testing codes for bugs, integrating models into the client ecosystem, and documenting project artifacts.

• *Key Responsibilities:**
• Connect with internal / external POC to understand the business requirements
• Coordinate with the right POC to gather all relevant data artifacts, anecdotes, and hypotheses
• Create project plans and sprints for milestones / deliverables
• Spin VM, create and optimize clusters for Data Science workflows
• Create data pipelines to ingest data effectively
• Assure the quality of data with proactive checks and resolve the gaps
• Carry out EDA, Feature Engineering & Define performance metrics prior to running relevant ML/DL algorithms
• Research whether similar solutions have been already developed before building ML models
• Create optimized data models to query relevant data efficiently
• Run relevant ML / DL algorithms for business goal seek
• Optimize and validate these ML / DL models to scale
• Create light applications, simulators, and scenario builders to help business consume the end outputs
• Create test cases and test the codes pre-production for possible bugs and resolve these bugs proactively
• Integrate and operationalize the models in the client ecosystem
• Document project artifacts and log failures and exceptions
• Measure, articulate impact of DS projects on business metrics and finetune the workflow based on feedbacks
• *Qualifications Required:**
• Graduate degree in a quantitative field (CS, statistics, applied mathematics, machine learning, or related discipline)
• Good programming skills in Python with strong working knowledge of Pythons numerical, data analysis, or AI frameworks such as NumPy, Pandas, Scikit-learn, etc.
• Experience with SQL, Excel, Tableau/ Power BI, PowerPoint
• Predictive modeling experience in Python (Time Series/ Multivariable/ Causal)
• Experience applying various machine learning techniques and understanding the key parameters that affect their performance
• Experience of building systems that capture and utilize large data sets to quantify performance via metrics or KPIs
• Excellent verbal and written communication
• Comfortable working in a dynamic, fast-paced, innovative environment with several ongoing concurrent projects As a Lead Data Scientist with 8-12 years of experience, you will be responsible for the following:
• *Role Overview:**

You will lead a team of Data Engineers, Analysts, and Data Scientists to execute various tasks including connecting with internal/external points of contact to understand business requirements, creating project plans and sprints for milestones, optimizing clusters for Data Science workflows, creating data pipelines, conducting Exploratory Data Analysis (EDA) and Feature Engineering, defining performance metrics, researching existing solutions, running machine learning (ML) algorithms, creating optimized data models, developing light applications and scenario builders, testing codes for bugs, integrating models into the client ecosystem, and documenting project artifacts.

• *Key Responsibilities:**
• Connect with internal / external POC to understand the business requirements
• Coordinate with the right POC to gather all relevant data artifacts, anecdotes, and hypotheses
• Create project plans and sprints for milestones / deliverables
• Spin VM, create and optimize clusters for Data Science workflows
• Create data pipelines to ingest data effectively
• Assure the quality of data with proactive checks and resolve the gaps
• Carry out EDA, Feature Engineering & Define performance metrics prior to running relevant ML/DL algorithms
• Research whether similar solutions have been already developed before building ML models
• Create optimized data models to query relevant data efficiently
• Run relevant ML / DL algorithms for business goal seek
• Optimize and validate these ML / DL models to scale
• Create light applications, simulators, and scenario builders to help business consume the end outputs
• Create test cases and test the codes pre-production for possible bugs and resolve these bugs proactively
• Integrate and operationalize the models in the client ecosystem
• Document project artifacts and log failures and exceptions
• Measure, articulate impact of DS projects on business metrics and finetune the workflow bas