AI / ML Data Scientist -I (P3)

At Nielsen, we are passionate about our work to power a better media future for all people by providing powerful insights that drive client decisions and deliver extraordinary results. Our talented, global workforce is dedicated to capturing audience engagement with content - wherever and whenever it’s consumed. Together, we are proudly rooted in our deep legacy as we stand at the forefront of the media revolution. When you join Nielsen, you will join a dynamic team committed to excellence, perseverance, and the ambition to make an impact together. We champion you, because when you succeed, we do too. We enable your best to power our future.

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.

● Programming Languages

○ Python (Expert): Strong proficiency in Python with extensive experience in libraries such as: Scikit-learn, Pandas & NumPy, Matplotlib, Seaborn, SciPy.

○ Deep Learning Libraries: Strong proficiency in Tensor flow, Keras and Pytorch. .

● Classical Machine Learning Expertise

○ 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).

○ Proficiency in unsupervised learning techniques (e.g., K-Means, hierarchical clustering, PCA).

○ Knowledge of ensemble methods and their practical application.

- 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.
- 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.
- Optimisation techniques: One of the positions should have Hands-on experience in optimisation and production grade optimiser.
- SQL: Solid proficiency in SQL for data extraction, manipulation, and analysis from relational databases.
- Version Control: Experience with Git and collaborative development workflows.
- 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.

F1-score, ROC-AUC, RMSE, MAE) to assess model performance and optimize models for production.

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,; 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.

Requirements

Strong understanding and experience in classical Machine Learning algorithms 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.; Bachelors of Master’s or Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, or a related quantitative field.; 3 to 10 years of hands-on experience in developing and deploying AI/ML models, with a strong focus on Computer Vision.; Proficiency in Python and deep learning frameworks such as PyTorch (preferred) or TensorFlow/Keras.; Demonstrable experience with Multi Modal Large Language Models (LLMs) and their application, including familiarity with transformer architectures and fine-tuning techniques.; Experience with developing simple UIs for model interaction or data annotation (e.g., using Streamlit, Gradio, Flask/Django).; Solid 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