DXFactor - Lead Data Scientist - Python/Big Data

Key Responsibilities :

- Lead the end-to-end lifecycle of data science projects, including problem definition, data exploration, feature engineering, model development, validation, and deployment.

- Architect and implement scalable and robust machine learning models and algorithms to address critical business problems.

- Guide and mentor a team of data scientists and analysts, fostering their technical growth and ensuring high-quality deliverables.

- Collaborate closely with product managers, engineers, and business stakeholders to understand their needs and translate them into actionable data science solutions.

- Communicate complex analytical insights and findings to both technical and non-technical audiences in a clear and concise manner.

- Drive innovation by researching and experimenting with new data science techniques, tools, and technologies.

- Ensure the reproducibility and maintainability of data science workflows and models.

- Contribute to the development of data science best practices and standards within the organization.

- Evaluate and select appropriate data sources, tools, and methodologies for specific projects.

- Stay up-to-date with the latest advancements in data science, machine learning, and artificial intelligence.

- Play a key role in shaping the data science roadmap and strategy for the company.

Required Skills :

- 7-9 years of hands-on experience in building and deploying machine learning models and statistical analyses in a business context.

- Strong proficiency in Python and relevant data science libraries (e.g., scikit-learn, TensorFlow, PyTorch, pandas, NumPy).

- Deep understanding of machine learning algorithms (e.g., regression, classification, clustering, deep learning)

and their underlying principles.

- Extensive experience with feature engineering, data cleaning, and data preprocessing techniques.

- Solid understanding of statistical inference, hypothesis testing, and experimental design.

- Experience with big data technologies and platforms (e.g., Spark, Hadoop, cloud-based data warehouses like Snowflake, BigQuery, Redshift) is highly desirable.

- Experience with model deployment and monitoring tools (e.g., MLflow, Kubeflow, SageMaker).

- Excellent communication and presentation skills with the ability to explain complex technical concepts to non-technical stakeholders.

- Strong problem-solving and analytical skills with a proven ability to translate business problems into data science solutions.

- Experience with version control systems (e.g., Git).

- Bachelor's or Master's degree in Computer Science, or a related quantitative field.

Preferred Skills :

- Experience with specific industry domains relevant to our business (e.g., e-commerce, finance, healthcare).

- Experience with natural language processing (NLP) or computer vision techniques.

- Experience with time series analysis and forecasting.

- Experience with A/B testing and causal inference.

- Familiarity with cloud platforms (e.g., AWS, Azure, GCP) and their data science services.

- Experience with building and deploying real-time machine learning systems.

- Leadership or mentorship experience within a data science team