We are seeking an experienced, leadership-driven Data Scientist to lead our data science initiatives and maintain the highest standards of analytical rigor, machine learning innovation, and data-driven strategy. In this senior role, you will oversee all aspects of the data lifecycle, including data exploration, predictive modeling, algorithmic development, and production deployment. If you have strong team leadership skills, deep knowledge of advanced statistical practices, and a passion for operational excellence and robust AI/ML solutions, this is an outstanding opportunity to shape the technical future and performance of our data-driven products.

Key Responsibilities
Team Leadership: Manage, mentor, motivate, and evaluate the entire data team, including data scientists, data engineers, and machine learning researchers.

Standards & Quality Control: Establish and enforce high modeling standards, data governance protocols, and validation frameworks; conduct regular model audits and technical reviews.

Operations Planning: Develop and manage experiment roadmaps, model development lifecycles (MLOps), and technical strategies to ensure efficient delivery and scalable insights.

Resource & Budget Management: Control cloud compute costs, data infrastructure expenses, and licensing for analytical tools; manage relationships with data providers and software vendors.

Stakeholder Collaboration: Collaborate with Product, Engineering, and Executive teams to translate complex business problems into actionable data solutions and predictive products.

Training & Development: Design and deliver comprehensive training programs on statistical modeling, data visualization, and machine learning; mentor junior scientists and team leads.

Compliance & Reliability: Ensure full adherence to data privacy regulations (e.g., GDPR, CCPA), ethical AI standards, and internal data security policies.

Reporting & Continuous Improvement: Prepare reports on model performance, business impact, and data quality; implement cutting-edge machine learning methodologies and automated pipeline optimizations.

Requirements & Qualifications
Education: Bachelor’s degree in Data Science, Statistics, Computer Science, Mathematics, or a related quantitative field is preferred. A Master’s or PhD is highly desirable.

Experience: 5–10+ years in data science or quantitative analysis, with at least 3–5 years in a technical leadership or team management capacity. Experience in high-growth tech environments or complex enterprise data ecosystems is highly desirable.

Technical & Management Skills:

Strong knowledge of Python/R, statistical modeling, machine learning frameworks (e.g., PyTorch, TensorFlow, Scikit-learn), and big data technologies (e.g., Spark, SQL, NoSQL).

Experience with MLOps workflows, CI/CD for data, and cloud-based analytical platforms (AWS/Azure/GCP).

Proven ability to lead high-performing teams and bridge the gap between technical complexity and business value.

Soft Skills:

Excellent leadership, communication, and interpersonal skills.

Strong problem-solving, decision-making, and organizational abilities.

High attention to detail with a focus on reproducibility and model performance.

Ability to work under pressure and manage multiple high-stakes analytical projects in a dynamic environment.

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