Senior Data Scientist

The Senior Data Scientist will work on data-driven initiatives to solve complex business challenges, leveraging advanced analytics, machine learning, and statistical modeling. This role requires expertise in translating data insights into actionable strategies and collaborating with cross-functional teams. Ideal candidates will have a strong background in analytics or tech-driven industries.

Responsibilities:

• Optimizing Ad Message Delivery: Develop and deploy ML models for tasks such as optimizing personalized ad delivery content and timing for better outcomes, predicting customer lifetime value (CLV), cross-channel customer modeling, customer segmentation, and deriving insights from delivery results.
• Personalization and Recommendation: Design and implement recommendation engines to enhance user engagement and brand trust, leveraging rule-based, classical ML-based, and deep learning-based approaches.
• Advanced Segmentation: Lead customer behavioral analysis using clustering techniques to drive personalized brand empowerment strategies.
• Data Engineering and EDA: Clean, preprocess, and validate large, complex datasets (structured/unstructured). Perform deep-dive Exploratory Data Analysis (EDA) to identify patterns, ensure data integrity, and set the initial strategic direction for analysis.
• KPI Design and Stakeholder Strategy: Partner with cross-functional teams (e. g., sales, customer success) in our company and clients' marketing representatives to define and propose business KPIs that are logically sound, reasonably challenging, and easily communicable to non-technical teams.
• Continuous Improvement Loops: Systematically identify bottlenecks in model accuracy through rigorous evaluation and error analysis; iterate on model architectures and data features to consistently hit and exceed target KPIs.
• Collaborative Engineering and Privacy: Maintain production-grade code repositories using GitHub, ensuring version control and documentation are integrated into the R& D process while adhering to data privacy and ethical AI practices.
• Cutting-Edge Research: Research and implement state-of-the-art techniques, including LLMs/Generative AI, NLP, and Deep Learning, to enhance business strategies and solve brand empowerment challenges.

Requirements:

• Education: Master's/PhD in statistics, computer science, econometrics, or related quantitative fields.
• Experience: 6 years in data science, with proven expertise in:
• End-to-End ML Lifecycle: Proven experience leading the entire ML lifecycle, e. g., data exploration, feature transformation, KPI definition, model training, model derivation, system implementation, performance evaluation, and deployment and maintenance of ML systems in production.
• Programming and Coding Quality: Expert-level proficiency in Python and SQL. Solid coding skills with attention to code quality, readability, and maintainability.
• Machine Learning and Statistics Expertise: Specialized knowledge of machine learning fundamentals (prediction, classification, regression, and unsupervised clustering for tabular and time-series data) and statistics. Practical experience applying common ML/AI libraries (Numpy, Scikit-learn, and PyTorch) to solve real business problems.
• Data and Analytics Tools: Deep practical knowledge of SQL databases (e. g., MySQL and PostgreSQL) and analytics warehouses/OLAP (e. g., Snowflake and Redshift).
• Analytical and Experimental Methods: Rigorous skills in offline evaluation, cross-validation techniques, and iterative hypothesis testing, including understanding A/B testing and basic statistical analysis.
• Cloud Platforms: Hands-on experience architecting and deploying solutions on major cloud providers (AWS, Azure, GCP), with primary usage being AWS.
• Development Environment and Collaboration: Advanced proficiency with Linux environments, VS Code, version control (Git/GitHub), containerization, and CI/CD practices (e. g., Docker, GitHub Actions). Proven ability to collaborate with engineering teams to integrate ML models into production environments.

Soft Skills:

• Exceptional stakeholder management (translating complex data into simple stories)
• Professional time management
• A relentless problem-solving mindset
• Ability to translate business questions into data-driven approaches.
• Ability to mentor junior members technically and non-technically to execute projects as a team.

Nice-to-Have Qualifications:

Recommendation Systems:

• Deep expertise in building recommendation models, including collaborative filtering, content-based filtering, matrix factorization, neural collaborative filtering (NCF), and two-tower models.

Advanced ML Engineering:

• Exposure to online serving, model deployment, and production ML workflows (e. g., feature pipelines, monitoring, model iteration).
• Experience with the design and development of microservice architecture (e. g., FastAPI, Flask).
• Ability to tune open-source deep learning models and familiarity with specialized models (e. g., embeddings, sequence models, multi-task learning).
• Experience in the development and operation of high-QPS, low-latency machine learning online APIs.

Data Engineering and Orchestration:

• Hands-on experience with workflow/DAG orchestration tools (e. g., Apache Airflow, AWS Step Functions) for building and operating scalable data pipelines.
• Experience with streaming and messaging technologies (e. g., Kinesis, Kafka).
• Hands-on experience with distributed training environments or large-scale data processing frameworks (e. g., Apache Hadoop, Apache Spark, MPI).

Specialized Domain and Business Acumen:

• Project experience in related domains such as ranking, search, ads, or personalization systems.
• Analysis and proposal skills derived from a deep understanding of business goals and domain knowledge.
• Understanding of user behavior analysis, growth metrics, and business-driven optimization.

Community and Research:

• Contributions to open-source projects, research publications, Kaggle competitions, or other demonstrated passion for applied machine learning.