Modelling & Data Science Executive

Job Description:

Key Responsibilities:

• Develop, implement, and maintain advanced analytical models using machine learning algorithms and GenAI applications
• Utilize various advanced analytics techniques to uncover trends, patterns, and insights from large and complex datasets.
• Collaborate with cross-functional teams to identify business needs and deliver data-driven solutions.
• Create visually compelling dashboards and reports to present findings to stakeholders.
• Continuously evaluate and improve existing analytics methodologies and models to enhance accuracy and performance.
• Stay abreast of industry trends and advancements in analytics and machine learning to drive innovation within the team.
• Mentor junior team members and contribute to knowledge sharing within the organization.

Basic Qualifications:

• Bachelor’s or Master’s degree in Data Science, Business Analytics, Mathematics, Statistics, or a related field.
• 3+ years of experience in advanced analytics, data science, machine learning, Generative AI or a related field.
• Strong experience with quantitative modeling, predictive analytics, text analytics, and forecasting methodologies
• Proficiency in SQL (or Google BigQuery), Python, visualization tools like Tableau/PowerBI
• Familiarity with the Retail/CPG/Tech industry and experience with product, transaction, and customer-level data.
• Excellent communication skills, both verbal and written, with the ability to convey complex concepts to non-technical stakeholders.
• Strong analytical and problem-solving skills, with an inquisitive mindset.

Desired Skills:

Proficient in the following advanced analytics techniques:

• Descriptive Analytics: Statistical analysis, data visualization.
• Predictive Analytics: Regression analysis, time series forecasting, classification techniques, market mix modeling
• Prescriptive Analytics: Optimization, simulation modeling.
• Text Analytics: Natural Language Processing (NLP), sentiment analysis.

Extensive knowledge of machine learning techniques, including:

• Supervised Learning: Linear regression, logistic regression, decision trees, support vector machines, random forests, gradient boosting machines among others
• Unsupervised Learning: K-means clustering, hierarchical clustering, principal component analysis (PCA), anomaly detection among others
• Reinforcement Learning: Q-learning, deep Q-networks, etc.

Experience with Generative AI and large language models (LLMs) for text generation, summarization, and conversational agents.

• Researching, loading and application of the best LLMs (GPT, Gemini, LLAMA, etc.) for various objectives
• Hyper parameter tuning
• Prompt Engineering
• Embedding & Vectorization
• Fine tuning

Proficiency in data visualization tools such as Tableau or Power BI.

Strong skills in data management, structuring, and harmonization to support analytical needs.

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