TechProjects | Data Scientist | hyderabad

2 weeks ago

hyderabad, India TechProjects Full time

Company Description

TechProjects is a rapidly growing System Integration (SI) and IT staffing company based in Hyderabad. We offer cost-effective IT solutions for diverse clients, including small businesses, large corporations, and government agencies in sectors like healthcare, supply chain, and education. With a focus on Enterprise Digital Transformation, Data Intelligence & Automation, cloud upgrades, and more, we ensure a personalized experience and maximum return on investment for our clients.


Role Description

This is a full-time on-site role for a Data Scientist at TechProjects India office lcoated in Orbit by AURO , next to Myhome Bhoja , Raydurg Hyderabad . The Data Scientist will be responsible for data analytics, data visualization, statistical analysis, and data science tasks on a day-to-day basis.


Working hours- Onsite from 2.00 to 100.00 Pm IST


Data Scientist


Position Overview

The IS Enterprise Data & Innovation team at C&S is looking for a dynamic and detail-oriented Data Scientist with strong data engineering skills, cloud computing expertise, and proficiency in AI/ML to join our team. This role focuses on building end-to-end data solutions, developing advanced machine learning models, and ensuring scalable and reliable data infrastructure in a cloud-first environment. These capabilities will include predictive, prescriptive, adaptive and generative applications for use cases supporting enterprise business units cross procurement, warehouse operations, commercial, transportation, finance and human resources.

Key Responsibilities
Required Skills:


●     Education : Bachelor's or Master’s degree in Computer Science, Data Science, Engineering, Mathematics, or a related field.

●     Experience in building and deploying machine learning models using tools like Scikit-learn, TensorFlow, or PyTorch.

●     Strong programming skills in Python, R, or similar, with expertise in SQL.

●     Proficiency in cloud platforms (AWS, Azure, Google Cloud) and data engineering tools such as Databricks, Snowflake, or BigQuery.

●     Hands-on experience with distributed computing and big data technologies (e.g., Apache Spark, Hadoop).

●     Familiarity with MLOps practices, including Docker, Kubernetes, and CI/CD pipelines.

Preferred Skills:

●     Experience with real-time data processing tools like Apache Kafka or AWS Kinesis.

●     Knowledge of visualization tools such as Tableau, Power BI, or Dash for data storytelling.

●     Certifications in cloud platforms or data engineering (e.g., AWS Certified Data Analytics Specialty).

●     Familiarity with APIs and web services to connect ML workflows with business applications.

●     Experience in a wholesale, retail or data-driven environment a plus

●     Experience or knowledge with Oracle a plus

Soft Skills:


●     Excellent problem-solving skills with a strong analytical mindset.

●     Strong communication skills to translate technical results to non-technical audiences.

●     Ability to work collaboratively in cross-functional teams and manage competing priorities.


1.Data Science & Machine Learning

●     Build, train, and deploy machine learning models to solve real-world business problems.

●     Conduct data exploration, feature selection, and engineering to optimize model performance.

●     Analyze and interpret large datasets to derive actionable insights using statistical and predictive techniques.

●     Stay updated with state-of-the-art advancements in AI/ML and identify opportunities to integrate these technologies.

2. Cloud Data Engineering

●     Design, develop, and maintain robust ETL/ELT pipelines to support AI/ML workflows and analytical applications.

●     Ensure data quality, integrity, and security across multiple sources and environments.

●     Optimize data storage and processing architectures in cloud ecosystems (e.g., AWS, Azure, Google Cloud).

●     Develop real-time data ingestion and processing frameworks using cloud-native tools.

3. Collaboration and Integration

●     Collaborate with data engineers, software developers, and product teams to align data science solutions with business goals.

●     Integrate ML models into production environments and ensure performance monitoring and retraining as needed.

●     Work with DevOps and cloud teams to implement CI/CD pipelines for AI/ML workflows.

4. Performance Optimization and Governance

●     Conduct root-cause analysis and resolve issues in data pipelines and models.

●     Implement best practices for scalable, secure, and cost-effective cloud usage.

●     Ensure compliance with data privacy, security regulations, and ethical AI standards.



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