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 understand and implement deep learning architectures (CNNs, LSTMs, Transformers) for tasks like image recognition, natural language processing, and sequence modeling.
Documentation & Communication: Document models, methodologies, and results clearly and concisely. Effectively communicate complex technical concepts to both technical and non-technical stakeholders.
Research & 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.
Required Qualifications: Bachelors of 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 Multi Modal Large Language Models (LLMs) and their application.
Experience with developing simple UIs for model interaction or data annotation (e.g., using Streamlit, Gradio, Flask/Django). Good understanding of MLOps principles and experience with tools for model deployment, monitoring, and lifecycle management (e.g., Docker, Kubernetes, Kubeflow, 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
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.
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