Role & responsibilities :

- Collect, process, and analyze large datasets to generate actionable business insights.

- Build, test, and deploy machine learning models for predictive and prescriptive analytics.

- Perform data cleaning, feature engineering, and statistical analysis to ensure high data quality.

- Develop dashboards and visualizations using Tableau, Power BI, or similar tools for business stakeholders.

- Collaborate with product, engineering, and business teams to translate requirements into data solutions.

- Implement scalable data pipelines and workflows using Python, SQL, and big data technologies.

- Monitor model performance and optimize algorithms for accuracy and efficiency.

- Stay updated with emerging technologies (AI/ML, NLP, Generative AI, MLOps) and propose innovative solutions.

Preferred Candidate Profile :

Skills & Qualifications :

1. Technical Skills :

- Programming : Proficiency in languages like Python, R, SQL. Familiarity with tools like Spark, Dask, and frameworks like TensorFlow, PyTorch, or Scikit-learn.

- Data Manipulation & Analysis : Expertise in libraries like Pandas, NumPy, and data wrangling techniques.

- Machine Learning : Solid understanding of machine learning algorithms (linear models, decision trees, ensemble methods, clustering, etc.).

- Deep Learning (for senior roles) : Experience with neural networks, CNNs, RNNs, transformers, etc.

- Big Data : Familiarity with Hadoop, Spark, and distributed computing environments.

- Visualization : Experience with visualization libraries (e.g., Matplotlib, Seaborn, Plotly, or Power BI/Tableau for presenting findings).

- Cloud Platforms : Familiarity with cloud services like AWS, GCP, or Azure for model deployment and data storage.

- Version Control & Collaboration Tools : Git, Docker, CI/CD pipelines for smooth collaboration and deployment.

2. Business & Domain Knowledge :

- Strong business acumen to understand the impact of models on business outcomes.

- Ability to work closely with domain experts to understand business objectives and data sources.

3. Soft Skills :

- Problem-Solving : Ability to break down complex business problems into solvable data science tasks.

- Communication : Strong written and verbal communication skills to explain technical concepts to non-technical stakeholders.

- Collaboration : Experience working in cross-functional teams (data engineers, product managers, etc.).

- Project Management : Ability to manage multiple projects, set priorities, and deliver within deadlines.

- Mentorship : Ability to mentor junior team members and help with their professional development.

4. Educational Background :

- Bachelors or Masters degree in Data Science, Computer Science, Engineering, Mathematics, Statistics, or a related field.

- PhD is often preferred for highly technical roles, especially in research-heavy or advanced ML/DL areas.

5. Certifications (Optional) :

- Certifications in Data Science (e.g., from Coursera, edX, or DataCamp) or cloud platforms (AWS, GCP).

- Specialized certifications in machine learning or deep learning may also be a plus (e.g., Google Professional Data Engineer, AWS Certified Machine Learning, etc.).