AI/ML Data Scientist I

Job Overview

Role: AI / ML Data Scientist I Location: Bengaluru Experience: 0–3 Years Qualification: relevant degree in data science, statistics, engineering, mathematics, operations research, information sciences, or related scientific disciplines Key Skills: Python, Spark, AWS, SQL, Artificial Intelligence, Machine Learning, Git, GitLab, Spotfire, Tableau, JIRA, Confluence

Job Description

Are you looking to build a career in Artificial Intelligence, Machine Learning, Data Science, and large-scale data analytics? Nielsen is hiring for the position of AI/ML Data Scientist I in Bengaluru. Candidates with 0–3 years of work experience and a relevant degree can apply. This opportunity is particularly relevant for early-career data scientists who want to work on real-world machine learning, data engineering, statistical methodology, cloud computing, and audience measurement problems. The role combines data science, software development, data engineering, artificial intelligence, machine learning, and statistical analysis. Candidates will work on measurement and planning solutions used by publishers, advertisers, and agencies while helping productionize data pipelines and machine learning models. The AI/ML Data Scientist I position is designed for candidates who want to work across the boundaries of data science, software engineering, and data engineering, taking data science projects through the complete lifecycle. This includes exploring and cleaning data, identifying useful variables, applying statistical and machine learning techniques, building data pipelines, deploying models, validating methodologies, monitoring production systems, and documenting the resulting methodologies and code. Candidates will also work on audience measurement problems such as trend analysis, missing-data imputation, sampling and representation, bias reduction, indirect estimation, and data integration.

Roles and Responsibilities

• Solution Development: Build measurement and planning solutions for publishers, advertisers, and agencies.
• AI Implementation: Identify opportunities where Artificial Intelligence can improve existing and new projects; implement AI-based solutions for relevant business and analytical problems.
• Data Science Lifecycle: Support reproducible data science projects from end to end; develop and maintain production data pipelines; deploy and maintain machine learning models in production environments.
• Collaboration: Work with cross-functional teams to productionize analytical methodologies.
• Validation & Optimization: Validate and optimize data science methodologies and models.
• Communication: Communicate methodology and research findings to technical and non-technical audiences.
• Research & Analysis: Support research related to cross-platform audience measurement; perform trend analysis and investigate patterns within large datasets.
• Data Handling: Work with missing-data imputation and representation techniques; support sampling and bias-reduction methodologies; work on indirect estimation and data integration problems; explore datasets to identify relevant variables and relationships; clean and prepare large datasets for analysis; apply dimension reduction techniques when appropriate; calculate distances and integrate survey data.
• Quality & Documentation: Evaluate analytical outputs to ensure accuracy and reliability; investigate quality escapes and fix issues in production code; document new methodologies, analytical approaches, and code.

Skills and Eligibility Criteria

Educational Background: Relevant degree in data science, statistics, engineering, mathematics, operations research, information sciences, or related scientific disciplines.

Experience: 0–3 years of professional experience.

Mandatory Technical Skills:

• Proficiency in Python (Programming)
• Knowledge of Apache Spark (Big Data)
• Familiarity with AWS and cloud computing (Cloud)
• Proficiency in SQL (Database)
• Knowledge of Artificial Intelligence and Machine Learning concepts (AI/ML)
• Understanding of statistical concepts and analytical methodologies (Statistics)
• Ability to manipulate, analyze, and interpret large datasets (Data Analysis)
• Experience with Git and GitLab or similar version-control systems (Version Control)
• Familiarity with dashboarding and visualization tools such as Spotfire or Tableau (Visualization)
• Familiarity with JIRA and Confluence (Project Tools)

Competencies:

• Strong ability to document code and analytical methodologies (Documentation)
• Strong written and verbal communication skills (Communication)
• Ability to work effectively with distributed and cross-functional teams (Teamwork)