Position - Data Scientist (Azure Cloud, PySpark, Databricks)
Location - Mumbai/Remote
Experience - 2.5+
We are seeking a driven and innovative Data Scientist with 2.5-5 years of hands-on experience in data science, with expertise in working with Azure Cloud, PySpark, and Databricks. The ideal candidate will assist in building data-driven models and support the development and deployment of machine learning models at scale. This is an excellent opportunity for a junior data scientist to grow and contribute to high-impact projects across the organization.
Key Responsibilities
· Data Exploration & Feature Engineering:
- Assist in exploring large datasets to identify key trends, correlations, and patterns that drive actionable insights.
- Collaborate with senior data scientists to develop, test, and refine feature engineering pipelines to enhance model performance.
- Work with structured and unstructured data to uncover useful features and insights for business problems such as customer segmentation, risk modeling, and fraud detection.
· Model Development & Support:
- Contribute to the development and training of machine learning models under the guidance of senior team members.
- Apply machine learning algorithms and statistical models to solve business challenges, including classification, regression, and clustering problems.
- Collaborate with the team to ensure that models are properly evaluated, validated, and optimized for scalability and performance.
· Data Wrangling & Preparation:
- Support the data preprocessing and transformation tasks using PySpark within Azure Databricks environments.
- Work with large-scale, complex datasets to clean, aggregate, and prepare data for analysis and modeling.
- Ensure data quality and accuracy through the creation and maintenance of robust ETL pipelines.
· Collaboration & Communication:
- Work closely with cross-functional teams (e.g., engineering, operations, and business analysts) to understand business requirements and translate them into data science solutions.
- Communicate insights, findings, and recommendations to both technical and non-technical stakeholders in a clear and concise manner.
· Continuous Learning & Innovation:
- Stay up to date with the latest developments in data science and machine learning technologies, particularly in the cloud (Azure) and big data space.
- Participate in learning opportunities, workshops, and training sessions to continuously improve skills and contribute to innovation within the team.
Skills & Qualifications
· Education:
- Bachelor’s or Master’s degree in Data Science, Statistics, Computer Science, or a related field.
· Experience:
- 2.5-5 years of experience in a data science or analytics role, with demonstrated experience in developing models and handling data at scale.
- Practical experience working with Azure Cloud services, Databricks, and PySpark is essential.
· Technical Skills:
- Proficiency in Python and PySpark for data analysis, model building, and pipeline development.
- Solid understanding of SQL for querying and manipulating large datasets.
- Hands-on experience with machine learning algorithms (e.g., decision trees, random forests, gradient boosting, clustering) and statistical methods.
- Experience working with Azure Machine Learning, Azure Data Lake, and similar cloud-based tools.
Preferred Experience
- Experience with Azure Databricks for processing large-scale datasets.
- Knowledge of key financial data science use cases such as customer segmentation, fraud detection, and risk modeling.
What we provide:
- Opportunities to develop and grow as an engineer. We are at the forefront of our industry, always expanding into new areas, and working with open-source and new technologies.
- A set of hardworking and dedicated peers.
- Growth and mentorship. We believe in growing engineers through ownership and leadership opportunities. We also believe mentors help both sides of the equation.
You are important to us, let’s stay connected!
Every individual comes with a different set of skills and qualities so even if you don’t tick all the boxes for the role today, we urge you to apply as there might be a suitable/unique role for you tomorrow!
Disclaimer
The Data Career is a data science job posting platform that curates and displays data science-related job listings from various companies, including those with whom we have established partnerships. We provide a centralized platform for job seekers to access and explore a wide range of data science job opportunities. Please note that while we strive to provide accurate and up-to-date information, the availability of job listings and the status of partnerships may change over time.
It's important to understand that The Data Career acts as an intermediary by presenting job listings and apply links on our platform. The application and selection process for each job posting is managed entirely by the respective hiring company. We encourage all users to thoroughly review the job descriptions, requirements, and application procedures provided by the hiring companies before applying.
While we do our best to ensure the quality and legitimacy of the job postings on our platform, we cannot guarantee the accuracy, completeness, or authenticity of the information provided by external companies. Users are advised to exercise due diligence, research the hiring companies, and verify the details before submitting applications or sharing personal information. We value transparency and aim to provide a valuable resource for both job seekers and companies seeking data science talent. If you have any questions or concerns about specific job listings or partnerships, please feel free to contact us for further assistance.
Thank you for using The Data Career as part of your job search journey. Your feedback is valuable in helping us continuously improve our platform and services.
