Data Scientist/ Sr. Data Scientist

Key Responsibilities:

Problem Analysis & Project Management:

Business Potential & AI Strategy:

• Identify business opportunities where data science and machine learning can create significant value.
• Guide the organization in understanding the strategic potential of AI and machine learning to transform business operations.

Collaborate Across Teams:

• Work closely with IT, business units, and analytics teams to understand constraints and align on project objectives.
• Prioritize, scope, and manage data science projects, ensuring alignment with business goals and KPIs.

Governance & Compliance:

• Define and communicate governance principles for data science projects, ensuring that ethical standards and regulatory compliance are met.

Data Collection & Integration:

Data Sources & Pipelines:

• Understand and acquire access to various data sources (e.g., SQL, graph databases), building data pipelines to support efficient and repeatable data science workflows.
• Document and catalog data pipelines for clarity and transparency across teams.

Data Integration:

• Ensure integration of diverse data sources for cohesive project execution and seamless analytics.

Data Exploration & Preparation:

• Statistical & Visualization Techniques:
  • Apply techniques like hierarchical clustering, principal components analysis (PCA), and other statistical methods to explore and understand data.
• Hypothesis Generation & Testing:
  • Generate hypotheses regarding business processes, test using quantitative methods, and present actionable findings.
• Business Understanding:
  • Collaborate with domain experts to gain deep insights into business mechanics and incorporate those insights into data analysis.

Machine Learning & Advanced Analytics:

• Model Development:
  • Apply machine learning and advanced analytics techniques to perform classification, prediction, and optimization tasks.
  • Implement and test various models such as regression models, random forests, neural networks (ANN, RNN, CNN), support vector machines (SVM), and deep learning models.
• Model Testing & Evaluation:
  • Conduct cross-validation, A/B testing, and other model validation techniques to assess the performance, fairness, and bias in models.
• Domain Integration:
  • Leverage domain expertise (e.g., finance, marketing, supply chain) to integrate business knowledge into ML solutions.

Operationalization & Deployment:

• Collaboration with Engineers:
  • Work with ML Data Engineers to evaluate and implement ML deployment options, ensuring smooth integration of models into production environments.
• Model Performance Management:
  • Implement monitoring tools to track model performance and health, ensuring continued optimization and accuracy.
• Champion/Challenger Tests:
  • Conduct champion/challenger testing (e.g., A/B tests) on live systems to measure model impact and effectiveness.

Other Responsibilities:

• Training & Knowledge Sharing:
  • Provide training to business and IT staff on basic data science principles and advanced techniques.
• Collaboration & Innovation:
  • Promote collaboration between data science teams within the organization and across external partnerships.
• Agile Delivery Methodology:
  • Lead data science initiatives within an agile framework, ensuring iterative and incremental delivery of data solutions.

Qualifications:

Education:

• Bachelor’s degree in Computer Science, Data Science, Operations Research, Statistics, Applied Mathematics, or a related field required. Master’s degree preferred.
• Specialization in machine learning (ML), artificial intelligence (AI), cognitive science, or data science is highly desired.

Experience:

• At least 3-5 years of experience in data science, with a proven track record of launching, planning, and executing data science projects.
• Experience building and deploying predictive models, web scraping, and scalable data pipelines in real-world applications.

Technical Skills:

• Expertise in Python, Jupyter Notebooks, and familiarity with other scripting languages such as R, SAS, or MATLAB.
• Proficient in data science platforms such as Azure Machine Learning, Snowflake, and Google Cloud ML. Experience with other platforms like AWS or SAS is a plus.
• Strong knowledge of statistical and data mining techniques, including generalized linear models, regression analysis, random forests, boosting methods, text mining, deep learning, and neural networks.
• Experience with open-source libraries (e.g., sklearn, TensorFlow, PyTorch) and data visualization tools (e.g., Plotly, Streamlit, Power BI, Tableau) is a plus.
• Solid understanding of database programming (SQL) and familiarity with NoSQL and graph databases.

Additional Skills:

• Familiarity with MLOps and ModelOps to operationalize ML models in production environments.
• Experience in implementing deep learning techniques such as ANN, RNN, CNN, and SVM.
• Strong ability to design and implement demand forecasting models, predictive maintenance, and fraud detection models.

Competencies & Characteristics:

Leadership:

• Strong leadership skills, with the ability to motivate and guide cross-functional teams, including both technical and non-technical stakeholders.

Analytical & Strategic Thinking:

• Excellent analytical thinking, problem-solving abilities, and the capacity to translate complex data insights into actionable business strategies.

Collaboration & Communication:

• Ability to collaborate across teams and effectively communicate technical concepts to both technical and non-technical audiences.

Agility & Flexibility:

• Comfortable working within an agile delivery methodology and managing fast-paced, evolving requirements.

Entrepreneurial Mindset:

• Motivated by achieving long-term business outcomes and willing to take ownership of key initiatives from ideation to implementation.

What We Offer:

Career Growth & Impact:

• An opportunity to shape the future of data science at our organization, with a chance to lead high-impact projects in marketing, supply chain, and finance analytics.

Collaborative Environment:

• Work alongside cross-functional teams in a dynamic, fast-paced environment, fostering innovation and creative problem-solving.

Competitive Compensation:

• Attractive salary, benefits, and performance-based incentives, along with a culture of work-life balance.

Learning & Development:

• Continuous learning opportunities and the ability to work with cutting-edge tools and technologies in machine learning and AI.