Lead / Staff - Data Scientist -Personalization

About Aplazo

Aplazo

40% of users lacking credit history

$110M in funding

Aplazo Story in Techcrunch : https://techcrunch.com/2024/05/13/aplazo/

About ML & AI Labs

ML & AI team is central to driving innovation and business impact

state-of-the-art credit risk and fraud models

advanced recommendation systems, activation models, and dynamic pricing engines

Highlights & Impact

• Lowest fraud rate in LATAM

  , powered by real-time ML inferencing

• Alternative data lending

  to drive inclusion for users with no credit history

• Reinforcement learning

  for personalized credit limit management

• Dynamic risk-based pricing

  to boost customer conversions

• End-to-end recommendation engine

  to tailor every user touchpoint

Key Responsibilities

• Work closely with Growth, B2C product, Marketing, Sales, and Engineering teams to identify and prioritize data science projects.
• Translate complex data insights into actionable for non-technical stakeholders.
• Create and maintain a roadmap for data science projects that support growth objectives and ensure alignment with company goals.
• Define the vision for data science initiatives related to customer science, marketing science, and conversational AI for better customer acquisition, engagement, satisfaction, and retention.
• Promote an AI-first culture within the organization by advocating for the use of data in decision-making processes.
• Evaluate and integrate cutting-edge technologies and methodologies to keep Aplazo at the forefront of innovation.
• Understand the end-to-end ML pipeline (data gathering to production).
• Conduct Data Analyses; your analyses will decide which policies we adopt, where we expand our business, and with whom we partner.
• Represent Aplazo in industry conferences, webinars, and other public forums.

Required Qualifications:

Experience:

• Experience in AI-customer, especially in B2C (Business-to-Consumer), marketing, customer support, and customer growth relevant experience.
• Experience in developing cutting-edge customer science models for comprehensive customer personalisation, segmentation, rewards and referral systems, conversational AI, and recommendation engines.
• Experience designing and implementing strategies to enhance customer personalisation to improve customer engagement and retention.
• a proven track record of developing personalised customer experiences through data analytics and predictive modeling.
• Experience in growth hacking strategies to drive user acquisition and engagement using AI.
• Experience with CDPs and leveraging them to unify customer data from various sources for better insights and personalised experiences.
• Successful implementation of A/B testing, user segmentation, and personalisation to optimise marketing campaigns and marketing effectiveness.
• Experience working in highly dynamic work environments with a steep learning curve.
• Experience in statistical modeling, machine learning, data mining, unstructured data analytics, and natural language processing. Sound understanding of - Bayesian Modeling, Classification Models, Cluster Analysis, Neural Networks, Nonparametric Methods, Multivariate Statistics, etc.
• Leveraging innovation in applying AI to improve customer experiences, including experimenting with emerging technologies and methodologies.

Years of experience:

• 6-12 years of experience in the technology sector, with a significant portion spent working directly with AI/ML technologies.
• Experience in leading and managing AI/ML projects and teams.
• Experience working with clients or customers in a leadership capacity, ideally in AI-focused roles.

Technical skills:

• Python programming skill is a must. Strong coding capabilities in ML and Deep learning.
• Knowledge of cloud platforms (e.g., AWS, Azure, Google Cloud) and their AI/ML services (e.g., SageMaker, Azure ML, Google AI Platform).
• Proficiency in project management tools (e.g., Jira, Trello) and agile methodologies (e.g., Scrum, Kanban) to manage AI projects effectively.
• Familiarity with database queries and data analysis processes.
• Expertise in NLP techniques and familiarity with tools and libraries like BERT, GPT, spaCy, and NLTK.
• Experience with various ML/DL algorithms and techniques.
• Ability to stay updated with the latest research in GenAI and deep learning.

Soft skills:

• Proven leadership experience, including team building, mentoring, and managing cross-functional teams.
• Strong project management skills with experience in Agile and Scrum methodologies.
• Excellent verbal and written communication skills.
• Ability to explain complex technical concepts to non-technical stakeholders and customers.
• Strategic Thinking: Ability to align AI strategies with business goals and customer needs.
• Detail-oriented, with the ability to work both independently and collaboratively.