Data Scientist / Machine Learning Specialist

Position Description:

Employees in this job function are responsible for predicting and/ or extracting meaningful trends/ patterns/ recommendations from raw data, leveraging data science methodologies including Machine Learning (ML), predictive modeling, math, statistics, advanced analytics, etc. Key Responsibilities:

1) Understand business requirements and analyze datasets to determine suitable approaches to meet analytic business needs and support data-driven decision-making

2) Design and implement data analysis and ML models, hypotheses, algorithms and experiments to support data driven decision-making

3) Apply various analytics techniques like data mining, predictive modeling, prescriptive modeling, math, statistics, advanced analytics, machine learning models and algorithms, etc.; to analyze data and uncover meaningful patterns, relationships, and trends

4) Design efficient data loading, data augmentation and data analysis techniques to enhance the accuracy and robustness of data science and machine learning models, including scalable models suitable for automation

5) Research, study and stay updated in the domain of data science, machine learning, analytics tools and techniques etc.; and continuously identify avenues for enhancing analysis efficiency, accuracy and robustness

Skills Required:

Data Science, Statistics, Communications, Problem Solving, Analytical, Machine Learning

Skills Preferred:

Cost Analysis, Google Cloud Platform, GCP, Mechanical Engineering

Experience Required:

Senior Associate Exp: 4 to 6 years experience in statistics and ML based model development

Experience Preferred:

• Experience working within the Automotive industry or with related data such as vehicle telematics, manufacturing quality, supply chain, or customer behavior in an automotive context. • Experience with GCP services such as GCP Big query, GCS, Cloud Run, Cloud Build, Cloud Source Repositories, Cloud Workflows • Proficiency with specific dashboarding and visualization tools such as Looker Studio, PowerBI, Qlik, or Tableau. • Experience with SQL for data querying and manipulation. • Familiarity with big data technologies (e.g., Spark, Hadoop). • Experience with MLOps practices and tools for deploying and managing models in production. • Advanced degree (PhD) in Statistics or a related quantitative field.

Education Required:

Bachelor's Degree

Education Preferred:

Master's Degree

Additional Information:

• Lead the end-to-end lifecycle of data science projects, from defining the business problem and exploring data to developing, validating, deploying, and monitoring models in production. • Apply advanced statistical methodologies and machine learning algorithms to analyze large, complex datasets (structured and unstructured) and extract meaningful patterns and insights. • Develop and implement robust, scalable, and automated processes for data analysis and model pipelines, leveraging cloud infrastructure. • Collaborate closely with business stakeholders and cross-functional teams to understand their analytical needs, translate them into technical requirements, and effectively communicate findings. • Create compelling and interactive dashboards and data visualizations to clearly present complex results and insights to both technical and non-technical audiences. • Stay up to date with the latest advancements in statistics, machine learning, and cloud technologies, and advocate for the adoption of best practices.