Data Scientists

Brief Background

The Data Scientist is expected to harness large volumes of complex data to generate actionable insights that fuel business strategy and innovation. This role requires expertise in advanced statistical methods, machine learning techniques, and data visualization to discover patterns, forecast outcomes, and support data-driven decision-making across the organization.

ROLES AND RESPONSIBILITIES

• Data Acquisition & Preparation: Collect, clean, and preprocess structured and unstructured data from a variety of internal and external sources including databases, APIs, logs, and documents.
• Exploratory Data Analysis (EDA): Investigate data sets to identify patterns, anomalies, trends, and correlations that inform hypotheses and model development.
• Model Development: Design, implement, and fine-tune statistical and machine learning models for tasks such as prediction, classification, segmentation, and recommendation.
• Model Evaluation & Optimization: Evaluate model performance using appropriate metrics (e.g., accuracy, precision, recall, AUC) and iterate to improve robustness and generalizability.
• Data Storytelling & Visualization: Translate complex model outputs into intuitive visualizations and business-relevant narratives using tools like Power BI, Tableau, or custom dashboards.
• Strategic Communication: Present findings and actionable recommendations to stakeholders in a clear and compelling manner, enabling informed decision-making across departments.
• Cross-functional Collaboration: Work closely with business, product, engineering, and operations teams to understand domain-specific challenges and deliver tailored data-driven solutions.
• Continuous Learning & Innovation: Stay abreast of the latest developments in data science, AI/ML frameworks, and big data technologies to bring cutting-edge methods into our workflows.

ESSENTIAL KNOWLEDGE AND SKILLS REQUIRED

• Deep proficiency in Python or R, along with fluency in SQL and familiarity with core data manipulation libraries such as NumPy, Pandas, and Scikit-learn.
• The ability to translate complex data outputs into clear, concise, and visually compelling insights, tailored to suit both technical and non-technical stakeholders.
• A solid grasp of business fundamentals to connect analytical findings with strategic decision-making.
• A naturally analytical mindset, with a passion for uncovering insights, solving problems, and discovering meaningful patterns within data.

EDUCATIONAL QUALIFICATIONS

• An advanced degree (Master’s or PhD) in a quantitative discipline such as Statistics, Mathematics, Computer Science, or a closely related field.

EXPERIENCE

• 3+ years of demonstrated experience in data science, showcasing strong command of statistical modeling and machine learning techniques.