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.