Job Overview

Birlasoft is looking for an Associate Data Scientist to work across Hyderabad, Bangalore, Pune, and Noida in a hybrid setup. The role is aimed at experienced data science professionals with 5 to 12 years of experience who can combine statistical analysis, machine learning, artificial intelligence, Python programming, and MLOps to solve practical business problems.

The position covers the complete machine learning lifecycle, from understanding data and defining analytical approaches to building models, deploying solutions, and monitoring them in production. The successful candidate will work with large datasets, data engineering teams, business stakeholders, architects, and product owners to create reliable and scalable AI and ML solutions.

This opportunity is relevant for professionals searching for Birlasoft Jobs, Data Scientist Jobs, Machine Learning Jobs, AI Jobs, MLOps Jobs, Python Jobs, or experienced IT Jobs in Hyderabad, Bangalore, Pune, and Noida.

Key Responsibilities

• Design and develop machine learning and AI solutions for practical business requirements.
• Analyze large datasets to identify patterns, trends, and useful business insights.
• Perform exploratory data analysis and statistical investigations before model development.
• Build predictive models using suitable machine learning and statistical techniques.
• Develop end-to-end ML workflows covering data preparation, feature engineering, training, validation, and deployment.
• Establish and improve MLOps practices for model delivery, monitoring, versioning, and governance.
• Work closely with data engineering teams to support scalable and dependable data pipelines.
• Evaluate model performance and improve results through appropriate tuning and optimization methods.
• Deploy machine learning models into production environments and monitor their reliability and performance.
• Collaborate with business teams, architects, product owners, and other technical stakeholders to translate requirements into workable data science solutions.
• Keep current with developments in artificial intelligence, machine learning, Generative AI, and modern data science platforms.
• Contribute to responsible and maintainable machine learning practices across the development lifecycle.

Required Skills

Candidates should have strong practical experience in Python-based data science and machine learning. The role requires the ability to work with data, develop analytical models, evaluate results, and move suitable solutions toward production.

Important skills include:

• Strong Python programming skills.
• Experience with NumPy and Pandas for data processing and analysis.
• Practical knowledge of Scikit-learn and at least one deep learning framework such as TensorFlow or PyTorch.
• Understanding of supervised and unsupervised machine learning.
• Knowledge of deep learning approaches and their practical applications.
• Strong statistical analysis and hypothesis testing skills.
• Experience with regression, classification, clustering, and forecasting.
• Predictive modeling and advanced analytics experience.
• Understanding of MLOps concepts, including model deployment, monitoring, versioning, and CI/CD.
• Strong SQL and data manipulation skills.
• Experience handling large-scale datasets and distributed computing environments.
• Familiarity with cloud platforms such as AWS, Microsoft Azure, or Google Cloud Platform.

Preferred Skills

Experience with modern data and AI platforms can strengthen a candidate's profile. Exposure to Databricks Mosaic AI, MLflow, and Delta Lake is useful for professionals working with modern machine learning workflows.

Knowledge of Snowflake Cortex or Snowflake ML capabilities is also considered valuable. Candidates with experience developing Generative AI or LLM-based applications can bring additional relevant expertise.

Additional useful areas include model explainability, fairness, responsible AI practices, governance frameworks, Docker, and Kubernetes. These skills can help when building machine learning solutions that need to operate reliably at scale.

Education

The job description does not specify a mandatory educational qualification. Candidates should demonstrate relevant professional expertise in data science, machine learning, statistics, computer science, engineering, mathematics, or a related technical area through their education and practical experience.

Experience

The stated experience range is 5 to 12 years. This is an experienced Data Scientist opportunity rather than a fresher position.

Candidates should demonstrate hands-on experience across machine learning, statistical analysis, predictive modeling, Python-based data science, and production-oriented ML workflows. Experience with MLOps and collaboration with data engineering teams is particularly relevant because the role extends beyond experimentation into deployment, monitoring, and operational support.

Required Technologies

The technology stack mentioned for this opportunity includes Python, NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch, SQL, AWS, Microsoft Azure, Google Cloud Platform, machine learning, artificial intelligence, MLOps, CI/CD, model versioning, model monitoring, distributed computing, and large-scale data processing.

Additional technologies and platforms include Databricks Mosaic AI, MLflow, Delta Lake, Snowflake Cortex, Snowflake ML, Generative AI, LLM applications, Docker, and Kubernetes.

Soft Skills

Strong communication and collaboration skills are important because the role involves working with technical and non-technical stakeholders. Candidates should be able to explain analytical findings, model behavior, assumptions, and technical trade-offs in a clear way.

A structured problem-solving approach is also important. Data scientists in this role need to work with ambiguous business questions, select appropriate analytical methods, validate results, and turn findings into practical solutions.

Curiosity and continuous learning are valuable because AI and ML technologies change quickly. The ability to learn new tools, evaluate emerging approaches, and work effectively with different teams will support success in this position.

Benefits of Working in this Role

This role provides broad exposure to the machine learning lifecycle, combining analytics, predictive modeling, AI, cloud technologies, and MLOps. Professionals can strengthen their experience in taking models from experimentation toward production use.

The opportunity to work with modern platforms such as Databricks and Snowflake, along with cloud environments and AI technologies, can also support long-term growth in machine learning engineering, data science, MLOps, and AI solution development.

Work Mode

This is a hybrid position. The role is available in Hyderabad, Bangalore, Pune, and Noida.

Location

Hyderabad, Telangana, India; Bangalore, Karnataka, India; Pune, Maharashtra, India; and Noida, Uttar Pradesh, India.

Who Should Apply

This opportunity is suitable for experienced Data Scientists, Machine Learning Engineers, AI professionals, and analytics-focused software professionals with 5 to 12 years of relevant experience.

Candidates should be comfortable developing machine learning models, performing statistical analysis, working with Python and SQL, handling large datasets, and supporting production-oriented ML workflows. Professionals with MLOps experience or exposure to cloud platforms should highlight those capabilities in their applications.

Candidates with experience in Databricks Mosaic AI, MLflow, Delta Lake, Snowflake Cortex, Generative AI, LLM applications, Docker, or Kubernetes may be especially well aligned with the preferred skills.

Career Growth

Experience in this position can support career progression toward Senior Data Scientist, Machine Learning Engineer, AI Engineer, MLOps Engineer, Applied Scientist, Data Science Lead, or AI Solution Architect roles.

Building expertise across model development, cloud platforms, MLOps, data engineering collaboration, and production monitoring can help professionals move toward broader technical leadership and AI solution ownership.

Application Advice

Customize your resume around measurable machine learning and data science work rather than listing technologies without context. Describe the business problems you solved, the datasets involved, the models you developed, and how you evaluated their results.

Highlight hands-on Python, SQL, statistical modeling, predictive analytics, and machine learning experience. If you have taken models into production, explain your role in deployment, monitoring, versioning, or model governance.

Also mention practical experience with AWS, Azure, or GCP. Candidates with Databricks, Mosaic AI, MLflow, Delta Lake, Snowflake Cortex, Generative AI, Docker, or Kubernetes exposure should include those skills clearly.

For professionals targeting Data Scientist Jobs, Machine Learning Jobs, AI Jobs, MLOps Jobs, Python Jobs, or Software Jobs in Hyderabad, Bangalore, Pune, and Noida, a focused resume showing both technical depth and business impact can make the application stronger.