Senior AI/ML Data Scientist I (P3)

At Nielsen, we are seeking a Data Scientist to join our team. Are you passionate about pushing the boundaries with the latest advancements in AI/ML? Does the prospect of applying Cutting-edge AI research to develop industry-defining software solutions for audience measurement excite you? In this role, you will be at the forefront of our mission, leveraging sophisticated machine learning and AI to deliver a comprehensive understanding of audience behavior. You will architect and implement AI/ML systems that unlock novel insights from complex audience data. Skills: Strong understanding and experience in Statistical Modelling and techniques. Demonstrated ability to work with high motivation and agility in a dynamic environment. Education: Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, Engineering, or a related quantitative field. Python (Expert): Strong proficiency in Python with extensive experience in libraries such as: Scikit-learn, Pandas & NumPy, Matplotlib, Seaborn, SciPy. Statistical Modeling: Strong grasp of statistical concepts including hypothesis testing, probability distributions, regression analysis, and inferential statistics. Data Preprocessing & Feature Engineering: Proven ability to handle missing data, outliers, categorical variables, scaling, normalization, and create impactful features from raw data. Proficiency in unsupervised learning techniques (e.g., K -Means, hierarchical clustering, PCA). Knowledge of ensemble methods and their practical application. SQL: Solid proficiency in SQL for data extraction, manipulation, and analysis from relational databases. Classical Machine Learning: Thorough understanding of supervised learning (e.g., Linear Regression, Logistic Regression, Decision Trees, Random Forests, Gradient Boosting Machines like XGBoost/LightGBM/CatBoost, SVMs, Naive Bayes, K-Nearest Neighbors). Model Evaluation & Validation: Hands-on experience with cross-validation, regularization techniques, hyperparameter tuning (e.g., GridSearchCV, RandomizedSearchCV), and understanding of various evaluation metrics for classification and regression. Version Control: Experience with Git and collaborative development workflows. ● Programming Languages: Python Problem -Solving: Excellent analytical and problem-solving skills with the ability to break down complex problems into manageable components. Communication: Strong verbal and written communication skills to articulate technical concepts and insights effectively.

Responsibilities: Model Development: Lead the development and implementation of data science solutions. Design, develop, train, and validate classical machine learning models (e.g., Regression, Classification, Clustering, Tree-based models like Random Forests, Gradient Boosting Machines, SVMs, etc.) to solve specific business problems. Data Preprocessing & Feature Engineering: Perform extensive data cleaning, transformation, and feature engineering to prepare diverse datasets for model training. Identify and create relevant features to improve model performance. Exploratory Data Analysis (EDA): Conduct thorough EDA to understand data characteristics, identify patterns, anomalies, and relationships, and inform model selection and development. Model Evaluation & Optimization: Implement rigorous model evaluation techniques (e.g., cross-validation, hyperparameter tuning) and metrics (e.g., accuracy, precision, recall, F1-score, ROC-AUC, RMSE, MAE) to assess model performance and optimize models for production. Production Deployment (MLOps Fundamentals): Collaborate with MLOps/DevOps teams to integrate, deploy, and monitor classical ML models in production environments. Understand basic concepts of model serving and API development. Algorithm Selection & Customization: Research and select appropriate classical ML algorithms based on problem type, data characteristics, and performance requirements. Deep Learning and Neural Networks: Move beyond traditional ML algorithms to u