Job
Description
Problem Definition: Partner with stakeholders to translate business goals into clear, data-focused questions and define project scope and success metrics. 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. Requirements: Bachelor s/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: Strong knowledge of Applied AI ML & Deep Learning Data Science techniques, Hardcore in 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. Advanced coding experience in at least one programming language (Python, Pyspark) & Strong experience in object-oriented concepts. Good to have advanced experience in one or more of the following: Spark, Databricks, Azure technical stack Good to have experience in model deployment to cloud/on-prem. Good Communication & presentation skills.
