Job Overview
Role: AI / ML Data Scientist I Location: Bengaluru, India Experience: 0–3 Years Qualification: Relevant degree in data science, statistics, engineering, mathematics, operations research, information sciences, or related scientific disciplines Key Skills: Python, Apache Spark, AWS, SQL, AI/ML, Statistics, Data Analysis, Data Pipelines, Production ML, Git
Job Description
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. Rather than focusing only on building machine learning models in a notebook, the role involves 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
- Build measurement and planning solutions: For publishers, advertisers, and agencies.
- Identify opportunities: Where Artificial Intelligence can improve existing and new projects.
- Implement AI-based solutions: For relevant business and analytical problems.
- Support reproducible data science projects: From end to end.
- Develop and maintain: Production data pipelines.
- Deploy and maintain: Machine learning models in production environments.
- Work with cross-functional teams: To productionize analytical methodologies.
- Validate and optimize: Data science methodologies and models.
- Communicate methodology and research findings: To technical and non-technical audiences.
- Support research related: To cross-platform audience measurement.
- Perform trend analysis: And investigate patterns within large datasets.
- 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.
- 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
- Knowledge of Apache Spark
- Familiarity with AWS and cloud computing
- Proficiency in SQL
- Knowledge of Artificial Intelligence and Machine Learning concepts
- Understanding of statistical concepts and analytical methodologies
- Ability to manipulate, analyze, and interpret large datasets
- Experience with Git and GitLab or similar version-control systems
- Familiarity with dashboarding and visualization tools such as Spotfire or Tableau
- Familiarity with JIRA and Confluence
Competencies:
- Strong ability to document code and analytical methodologies
- Strong written and verbal communication skills
- Ability to work effectively with distributed and cross-functional teams