Principal Data Scientist

Onsite role to NJ from day one. Only Permanent Residents | No Sponsorship Available

Job Summary

We are looking for a passionate and analytical Data Scientist to join our growing Data & AI team. The ideal candidate will have strong expertise in machine learning, statistical analysis, data modelling, Python-based data science ecosystems, and cloud-based analytics platforms.

The Data Scientist will work with business stakeholders, data engineers, and product teams to build predictive models, generate actionable insights, and develop AI/ML solutions that drive strategic business outcomes.

Key Responsibilities

Analyse large, complex, and structured/unstructured datasets to identify trends, patterns, and business opportunities.

Design, develop, validate, and deploy machine learning and statistical models for prediction, classification, recommendation, forecasting, and optimisation use cases.

Perform data cleaning, feature engineering, exploratory data analysis (EDA), and model evaluation.

Develop scalable data science solutions using Python, SQL, and cloud-native technologies.

Collaborate with data engineering teams to define data requirements, pipelines, and model integration strategies.

Build and maintain dashboards, visualisations, and reports to communicate insights to technical and non-technical stakeholders.

Conduct A/B testing, hypothesis testing, and statistical experimentation to support business decisions.

Monitor model performance, detect model drift, and implement continuous improvement processes.

Participate in AI/ML architecture discussions, code reviews, and best-practice initiatives.

Prepare technical documentation, model documentation, and knowledge-sharing artefacts.

Required Skills & Qualifications

3+ years of experience in Data Science, Machine Learning, or Advanced Analytics.

Strong programming skills in Python.

Hands-on experience with:

Pandas

NumPy

Scikit-learn

SciPy

Matplotlib / Seaborn / Plotly

Strong proficiency in SQL for data extraction and analysis.

Solid understanding of:

Supervised and Unsupervised Learning

Regression and Classification

Clustering

Time Series Forecasting

Feature Engineering

Model Evaluation Metrics

Statistics and Probability

Experience working with large-scale datasets and distributed data environments.

Ability to translate business problems into analytical and machine learning solutions.

Preferred

Experience with Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), or Agentic AI frameworks.

Exposure to AWS, Azure, or Google Cloud Platform data and AI services.

Familiarity with Spark / PySpark and big data processing frameworks.

Experience with MLflow, SageMaker, Vertex AI, Azure ML, or Databricks.

Knowledge of Docker, Kubernetes, and MLOps practices is an added advantage.

Desired Competencies

Strong analytical and problem-solving mindset.

Excellent communication and data storytelling skills.

Ability to work independently and in cross-functional agile teams.

Business-oriented thinking with a focus on measurable outcomes.

Curiosity and willingness to learn emerging AI/ML and Generative AI technologies.

Education

Bachelor s or Master s degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related quantitative field.

Advanced certifications in Machine Learning, Data Science, or Cloud AI platforms are highly desirable.

Preferred Certifications

AWS Certified Machine Learning Specialty

Microsoft Certified: Azure Data Scientist Associate

Google Professional Machine Learning Engineer

Relevant certifications from Coursera, DeepLearning.AI, Databricks, or Udacity.

Nice to Have

Experience in Healthcare, Life Sciences, Banking, Retail, Manufacturing, or Digital Product Analytics domains.

Exposure to NLP, computer vision, recommendation systems, or graph analytics.

Familiarity with Power BI, Tableau, or QuickSight.

Experience working in Agile / Scrum delivery environments.

What We Offer

Opportunity to work on cutting-edge AI, Machine Learning, and Generative AI initiatives.

Exposure to enterprise-scale cloud and analytics platforms.

Collaborative, innovation-driven, and learning-focused work culture.

Support for certifications, research, experimentation, and professional growth.