Highlights
Scalable solutions, AI/ML expertise, hands-on experience, cross-functional collaboration, data visualization
Description
Job Summary
pADA, a leading data and artificial intelligence company, seeks a Data Scientist with expertise in machine learning and AI to design scalable data-driven solutions. The ideal candidate will have hands-on experience in predictive modeling, large dataset analysis, and applying AI techniques.
Responsibilities
- Develop and implement machine learning models for business problems.
- Perform data cleaning, feature engineering, and exploratory data analysis on structured and unstructured datasets.
- Build predictive models using regression, classification, clustering, NLP, and deep learning techniques.
- Deploy models into production using MLOps best practices.
- Collaborate with cross-functional teams to translate requirements into technical solutions.
- Monitor model performance and continuously optimize and retrain models.
- Work with cloud platforms for scalable data processing.
- Present insights and recommendations through data visualization techniques.
Required Skills
- Python / R
- SQL
- NLP
- Deep Learning (TensorFlow / PyTorch)
- Data Visualization
Required Skills Explained
- Python / R: Essential for data manipulation, model development, and automation.
- SQL: Critical for querying and managing large datasets.
- Supervised & Unsupervised Learning: Core techniques for training models on both labeled and unlabeled data.
- Feature Engineering: The process of selecting, creating, and transforming raw features into meaningful representations for model building.
- NLP (Natural Language Processing): Crucial for handling text-based data in machine learning tasks.
- Deep Learning (TensorFlow / PyTorch): Advanced techniques for building complex neural networks to solve intricate problems.
- Pandas, NumPy, Scikit-learn: Libraries and frameworks for efficient data manipulation and model implementation.
- Spark / PySpark: Tools for big data processing in a distributed computing environment.
- Power BI / Tableau / Matplotlib / Seaborn: Visualization tools to present insights effectively.
- AWS / Azure / GCP (any one preferred): Cloud platforms for scalable model deployment and management.
Who is this for
pThis role is ideal for candidates with a strong background in data science, machine learning, and artificial intelligence. Experience in real-world business problem-solving and MLOps is highly desirable.
Why This Job is a Good Opportunity
ulliWork with cutting-edge technologies in data science, AI, and machine learning at a leading company.liPotential to make significant impacts on business through data-driven solutions.liOpportunities for collaboration across various departments, enhancing your technical and soft skills.liGrowth within ADA's rapidly expanding global operations and diverse client base.
Interview Preparation Tips
- Revise Python / R scripts and SQL queries relevant to data science projects.
- PRACTICE explaining complex concepts in simple terms, especially NLP and deep learning techniques.
- Prepare examples of model evaluation techniques and optimization strategies you have used in previous roles.
- Be ready to discuss your experience with MLOps, Docker, and Kubernetes if relevant.
Career Growth in This Role
pThe career path for a Data Scientist at ADA can be highly rewarding. With hands-on experience in AI/ML projects, you'll have opportunities to lead more complex initiatives as you advance. Specializing further in NLP or deep learning could open doors to specialized roles such as AI engineer or data science team leader.pADA's global presence also offers international exposure and the chance to work with a variety of industries, broadening your skill set and industry knowledge.
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Skills
Frequently Asked Questions
What is the preferred experience level for this role?We are looking for candidates with 2-5 years of hands-on experience.
Is experience with cloud platforms necessary?Yes, working knowledge of AWS, Azure, or GCP is preferred.
What tools should I be proficient in for data visualization?Power BI, Tableau, Matplotlib, and Seaborn are recommended.
