Description
We are seeking a highly experienced Senior Data Scientist to lead the design, development, and implementation of advanced data science solutions that enable data-driven decision-making across the organization.
- The ideal candidate combines deep expertise in data engineering, machine learning, and statistical modeling with strong leadership skills to mentor teams, influence business strategy, and build scalable AI/ML-driven systems.
Key Responsibilities
- Lead the end-to-end lifecycle of data science projects from data exploration and model development to deployment and monitoring.
- Architect and implement predictive modeling, machine learning, and AI solutions to address complex business challenges.
- Guide data preprocessing, feature engineering, and validation for structured and unstructured datasets.
- Collaborate with cross-functional teams (Business, Product, and Engineering) to translate business objectives into data-driven strategies.
- Design and enhance data pipelines and collection frameworks to improve data availability and quality.
- Conduct exploratory data analysis (EDA) to identify trends, patterns, and opportunities for innovation.
- Evaluate and select appropriate ML algorithms, optimize models for accuracy and scalability, and ensure production readiness.
- Present analytical insights and recommendations to senior leadership in a clear and actionable format.
- Mentor junior data scientists and establish best practices in coding, modeling, and experimentation.
- Stay current with emerging tools, technologies, and methodologies in data science and AI.
Required Skills & Expertise
- 7+ years of professional experience in data science and machine learning, including model design, validation, and deployment.
- Strong proficiency in Python (pandas, scikit-learn, TensorFlow, PyTorch) and statistical programming (R preferred).
- Advanced knowledge of SQL and experience with big data tools such as Hive, Spark, or Pig.
- Strong understanding of machine learning techniques (e.g., SVM, Random Forests, XGBoost, Neural Networks).
- Solid foundation in statistics, probability, and mathematics (linear algebra, calculus, optimization).
- Experience with cloud data platforms (AWS, Azure, GCP) and MLOps frameworks for scalable deployment.
- Expertise in data wrangling, feature selection, and handling large, imperfect datasets.
- Excellent communication skills with the ability to explain complex concepts to both technical and non-technical stakeholders
(ref:hirist.tech)
