Senior Data Scientist

JOB TITLE: Senior Data Scientist

DEPARTMENT: Software Development

REPORTS TO: Project Director

PURPOSE:

We are seeking a Senior Data Scientist to lead end-to-end data science projects, build predictive and prescriptive models, and mentor a growing team of data professionals. If you have a strong background in machine learning, statistical analysis, and big data technologies—and are passionate about driving business impact through data-driven insights—this is the opportunity for you.

KEY RESPONSIBILTIES:

• Lead Data Science Projects: Lead the design, development, and deployment of data science projects, from defining project objectives to identifying relevant datasets, developing models, and ensuring successful deployment. Apply advanced statistical techniques and machine learning algorithms to large, complex datasets to generate actionable insights for business decision-making.
• Model Development & Analysis: Design predictive and prescriptive models using techniques like decision trees, random forests, gradient boosting, and deep learning (TensorFlow, PyTorch) to optimize processes, forecast trends, and uncover new growth opportunities. Collaborate with cross-functional teams to develop and implement data-driven strategies that directly impact business performance.
• Mentorship & Team Development: Mentor and guide junior data scientists and analysts, providing technical direction, feedback, and support to elevate team expertise and improve overall performance. Foster a collaborative environment to share best practices, enhance technical capabilities, and ensure adherence to high standards in model development and deployment.
• Collaboration & Strategy Alignment: Collaborate closely with product, engineering, and business teams to understand requirements and deliver data science-driven solutions that align with business goals. Effectively communicate complex data science concepts and analytical findings to non-technical stakeholders, driving strategic decisions and influencing company-wide initiatives.
• Model Monitoring & Maintenance: Implement and maintain robust model monitoring processes to ensure model performance and accuracy over time, and address issues as they arise. Ensure that the team follows best practices in model evaluation, A/B testing, and data hygiene.
• Innovation & Continuous Learning: Stay current with advancements in data science, machine learning, and big data technologies, applying innovative techniques to improve models and address evolving business challenges. Contribute to the growth of the data science discipline within the company by sharing knowledge, introducing new tools, and refining workflows.
• Cloud Platforms: Experience working with cloud platforms for data science (AWS, Google Cloud, Azure) and big data ecosystems.
• Deep Learning & NLP: Knowledge of deep learning architectures (e.g., TensorFlow, PyTorch) and NLP frameworks (e.g., BERT, SpaCy, Hugging Face) for text and unstructured data applications.
• Data Visualization: Experience with data visualization tools (e.g., Tableau, Power BI) and libraries (e.g., Matplotlib, Seaborn) to communicate data insights effectively.
• Model Interpretability: Familiarity with model interpretability tools such as SHAP and LIME to explain and validate complex machine learning models.

QUALIFICATIONS, SKILLS, AND EXPERIENCE:

• At least Master’s or Ph.D. in Data Science, Statistics, Computer Science, Mathematics, or a related field.
  
• 5-7 years of hands-on experience in data science, including statistical analysis, model development, and project leadership.
• Expertise in machine learning algorithms (e.g., decision trees, random forests, gradient boosting, deep learning) and statistical techniques like hypothesis testing and A/B testing.
• Strong experience in data manipulation and transformation using Python (Pandas, NumPy) and SQL.
• Familiarity with big data technologies such as Spark and Hadoop for processing large-scale datasets.
• Proven ability to build, deploy, and maintain machine learning models in production environments, with strong knowledge of model monitoring and maintenance processes.