Job Description

Job Description

We are seeking an experienced and highly analytical Data Scientist with a strong statistical background to join our dynamic team. You will be instrumental in leveraging our rich datasets to uncover insights, build sophisticated predictive models, and create impactful visualizations that drive strategic decisions.
Work Location: Chennai, TN

Responsibilities

• 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 analyse 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.
  

  
  
  

Soft Skills

• Strong communication and collaboration skills.

  
  
  

• Ability to work in a fast-paced, agile environment.

  
  
  

• Proactive attitude and start-up mindset.

  
  
  

Technical Requirements

• 4+ years of progressive professional experience in a Data Scientist, Machine Learning Engineer, or similar quantitative role, with a track record of successfully delivering data science projects.

  
  
  

• Bachelor's or Master's degree in Statistics. A strong foundation in statistical theory and application is essential for this role. (We might consider highly related quantitative fields like Applied Statistics, Econometrics, or Mathematical Statistics if they have a demonstrably strong statistical core, but Statistics is our primary focus).

  
  
  

• Proven hands-on experience applying a variety of machine learning techniques (e.g., regression, classification, clustering, tree-based models, potentially deep learning) to real-world business problems.

  
  
  

• Must have strong proficiency in Python and its data science ecosystem (e.g., Pandas, NumPy, Scikit-learn, potentially TensorFlow or PyTorch).

  
  
  

• Hands-on experience working with cloud computing platforms (e.g., AWS, Azure, GCP) for data storage, processing, and deploying analytical solutions.

  
  
  

• Extensive experience creating data visualizations and dashboards to effectively communicate insights. You know how to tell a story with data!

  
  
  

• Solid understanding of experimental design, hypothesis testing, and statistical inference.

  
  
  

• Excellent problem-solving skills, attention to detail, and the ability to work with complex data structures.

  
  
  

• Strong communication, presentation, and interpersonal skills, with the ability to explain technical concepts clearly to diverse audiences.
  

  
  
  

Good to Have

• 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 BigQuery, GCS, Cloud Run, Cloud Build, Cloud Source Repositories, Cloud Workflows.

  
  
  

• Proficiency with specific dashboarding and visualization tools such as Looker Studio, Power BI, Qlik.

  
  
  

• 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.