Senior Data Scientist

At Nielsen, we are seeking a Data Scientist to join our team. 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.

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, and tree-based models like random forests, gradient boosting machines, SVMs, etc. ) to solve specific business problems.
• Data Preprocessing and 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 and 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 production models.
• 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 and 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 understand and implement deep learning architectures (CNNs, LSTMs, and Transformers) for tasks like image recognition, natural language processing, and sequence modeling.

Documentation and Communication:

• Document models, methodologies, and results clearly and concisely.
• Effectively communicate complex technical concepts to both technical and non-technical stakeholders.

Research and Innovation:

• Stay updated with the latest advancements in classical machine learning, statistical modeling, and data science best practices.
• Mentor junior data scientists and contribute to a culture of continuous learning and improvement.

Collaboration:

• Collaborate with cross-functional teams to define project requirements and deliver impactful results.
• Work closely with data scientists, data engineers, product managers, and business analysts to define problems, gather requirements, and deliver impactful ML solutions.

Requirements:

• Bachelor's, master's, or Ph. D. in computer science, artificial intelligence, machine learning, or a related quantitative field.
• 5 to 9 years of hands-on experience in developing and deploying AI/ML models.
• Proficiency in Python: Demonstrable experience with multimodal Large Language Models (LLMs) and their application.
• Experience with developing simple UIs for model interaction or data annotation (e. g., using Streamlit, Gradio, or Flask/Django). Good understanding of MLOps principles and experience with tools for model deployment, monitoring, and lifecycle management (e. g., Docker, Kubernetes, Kubeflow, and MLflow).
• Strong software engineering fundamentals, including code versioning (Git), testing, and CI/CD practices.
• Excellent problem-solving skills and the ability to work with complex, large-scale datasets.
• Strong communication and collaboration skills, with the ability to convey complex technical concepts to diverse audiences.
• Full-stack development experience in any one stack
• Skills: Strong understanding and experience in statistical modeling 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, and SciPy.
• Statistical Modeling: Strong grasp of statistical concepts, including hypothesis testing, probability distributions, regression analysis, and inferential statistics.
• Data Preprocessing and Feature Engineering: Proven ability to handle missing data, outliers, and categorical variables; scaling, normalization, and creating 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, and K-nearest neighbors).

Model Evaluation and 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.

Preferred Qualifications / Bonus Skills:

• Experience with generative AI models.
• Track record of publications in top-tier AI/ML/CV conferences or journals.
• Experience working with sports data (broadcast feeds, social media imagery, sponsorship analytics).
• Proficiency in cloud computing platforms (AWS, GCP, Azure) and their AI/ML services.
• Experience with video processing and analysis techniques.
• Familiarity with data pipeline and distributed computing tools (e. g., Apache Spark, Kafka).
• Demonstrated ability to lead technical projects and mentor team members.