• Whiteboard hypothesis matrix and solution architecture design along with solution architect
• Data Collection and Preparation: Gather, clean, and preprocess data from diverse sources, ensuring quality and consistency, and engineer features to enhance model performance.
• Exploratory Data Analysis (EDA): Use statistical methods and visualization to uncover trends and validate assumptions, summarizing key insights for business alignment.
• Model Building and Evaluation: Select, train, and refine models suited to the business problem, evaluating performance with relevant metrics and documenting model assumptions.
• Deployment and Monitoring: Work with engineering teams to deploy models, establish performance monitoring, retrain as necessary, and incorporate feedback to improve accuracy.

*Qualifications for Internal Candidates

Requirements:
•    Master’s in data science, Statistics, Computer Science, or related field.
•    Experience in end to end model development to deployment and performance monitoring.
•    Strong communication skills to convey insights to technical and non-technical audiences.

Skills Needed:

At least 5 end to end involvement in advanced analytics topics including 2 on structured  Data – these should be models that are production deployed and not a PoC. 
Strong knowledge of Applied AI ML & Deep Learning Data Science techniques,  ANN /Deep Learning /Machine Learning/NLP
Deep knowledge about machine learning algorithms such as tree-based methods, clustering, regression and classification, dimension reduction techniques, linear regression, Logistic regression, k-means, time series forecasting, Hypothesis testing (ANOVA, t-test, etc.), random forest, SVMs, Naive Bayes, gradient boosting, kNN, Deep learning algorithms like CNN, ANN and Reinforcement learning, Anomaly detection.
In-depth understanding of Statistical concepts e.g. Probability distributions, statistical tests, correlation analysis, descriptive statistics, kernels, ROC, F1-Score etc.
programming language (Python, Pyspark) & object-oriented concepts.
Spark, Databricks, Azure technical stack
model deployment to cloud/on-prem.
Good Communication & presentation skills. 

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