For Machine Learning : Location: Delhi NCR

Institution: Indian School of Business & Finance (ISBF)

Join ISBF and teach on globally recognised University of London degree programmes with academic direction from the London School of Economics and Political Science.

We are seeking a faculty member with expertise in Machine Learning.

Essential

Requirements

Expertise in Machine Learning.

Proficiency in Python, R, or equivalent analytical tools.

Strong understanding of

Applied Statistical Modelling.

Predictive Analytics.

Regression and Classification Techniques.

Supervised and Unsupervised Machine Learning.

Data Mining and Feature Engineering.

Model Evaluation and Validation.

Business Intelligence and Data Visualisation.

Excellent analytical, critical thinking, communication, and presentation skills.

Ability to contribute to teaching, curriculum development, student mentoring, and academic initiatives.

Qualifications

Master's degree in Data Science, Computer Science, Statistics, Mathematics, Artificial Intelligence, Business Analytics, or a related discipline.

PhD in a relevant field is highly desirable.

Prior teaching, research, or industry experience in analytics, AI, or data science will be an added advantage. If you are passionate about applying data-driven methods to solve real-world business problems, we'd love to hear from you.

Interested candidates are invited to apply with their CV and a brief cover letter. Faculty - Advanced Statistics Location: Delhi NCR

Institution: Indian School of Business & Finance (ISBF)

Employment Type: Full-Time, Permanent

Join ISBF and contribute to delivering globally recognised University of London degree programmes with academic direction from the London School of Economics and Political Science.

We are seeking a committed and academically strong Faculty member in Advanced Statistics with expertise in Probability, Distribution Theory, Mathematical Statistics and Statistical Inference.

The successful candidate will be responsible not only for high-quality teaching, but also for student mentoring, assessment, curriculum development, academic administration, research and broader institutional initiatives.

The Advanced Statistics curriculum provides students with a rigorous foundation in probability and statistical inference, including probability distributions, random variables, multivariate distributions, conditional distributions, estimation, likelihood and hypothesis testing.

Essential

Requirements

Strong academic expertise in Advanced Statistics, Mathematical Statistics, Probability and Statistical Inference.

Strong understanding of

Probability theory and probability spaces.

Conditional probability and Bayes' theorem.

Random variables and distribution functions.

Discrete and continuous probability distributions.

Expectation, variance, moments and generating functions.

Functions and transformations of random variables.

Convergence concepts.

Joint, marginal and multivariate distributions.

Conditional distributions and conditional expectations.

Independence, dependence and correlation.

Multivariate normal distributions.

Strong expertise in Statistical Inference,



including:

Populations, samples and statistics.

Data reduction and sufficient/minimal sufficient statistics.

Likelihood and log-likelihood.

Score functions and Fisher information.

Point estimation.

Bias, variance, mean squared error and consistency.

Central Limit Theorem.

Method of moments.

Order statistics.

Minimum variance unbiased estimation.

Cramér–Rao lower bound.

Maximum likelihood estimation.

Interval estimation and confidence intervals.

Pivotal functions.

Hypothesis testing.

Most powerful tests and Neyman–Pearson lemma.

Likelihood ratio tests.

These areas closely reflect the University of London subject guide, which expects students to develop a theoretical grounding in statistical inference and the ability to select appropriate methods of inference for real problems. Qualifications

Master's degree in Statistics, Mathematical Statistics, Mathematics, Econometrics, Data Science, or a closely related discipline.

PhD in Statistics, Mathematical Statistics, Mathematics, Econometrics or a related field is highly desirable.

Strong academic background in probability and statistics.

Prior experience teaching undergraduate-level statistics, mathematical statistics, probability, econometrics or related quantitative subjects.

Experience teaching advanced statistics at undergraduate level will be an advantage.

Proficiency in R, Python, MATLAB, Stata or equivalent statistical/computational tools will be an added advantage. Teaching & Academic Responsibilities The faculty member will:

Deliver engaging, rigorous and student-focused teaching across Advanced Statistics and related quantitative subjects.

Teach concepts at the appropriate level of mathematical and statistical depth while making them accessible to undergraduate students.

Develop lectures, tutorials, problem sets, learning activities and other teaching resources.

Use real-world examples and applications to demonstrate the relevance of statistical theory.

Conduct tutorials, doubt-clearing sessions and academic support activities.

Design, evaluate and grade assignments, tests, examinations and other assessments.

Provide timely and constructive feedback to students.

Support students in developing analytical, quantitative and statistical reasoning skills.

Prepare students effectively for University of London assessments and examinations.

Maintain high academic standards in line with University of London curriculum requirements.

The subject guides emphasise sequential learning, problem-solving practice and the ability to apply theoretical concepts to practical problems.

Student

Mentoring & Academic Engagement The faculty member will also be expected to:

Mentor and advise students on academic progress, subject choices,



projects and career development.

Identify students requiring additional academic support and provide appropriate interventions.

Guide students in developing quantitative and analytical capabilities.

Support student projects, dissertations and independent research where relevant.

Participate in academic advising and student engagement initiatives.

Contribute to a positive and intellectually stimulating learning environment. Curriculum & Academic Development

Contribute to curriculum development and academic planning within the programme.

Review and enhance course content, teaching methodologies and learning resources.

Participate in faculty meetings, programme reviews and academic committees.

Contribute to the development of assessments, question banks and examination materials.

Ensure effective alignment between teaching, learning outcomes and assessment.

Participate in academic quality assurance and programme enhancement initiatives. Research & Professional Development

Engage in research and scholarly activities in statistics, mathematical statistics, econometrics, data science or related areas.

Contribute to research publications, conferences, seminars and academic collaborations.

Stay current with developments in statistical theory, quantitative methods and their applications.

Encourage a culture of research and intellectual engagement among students.

Pursue continuous professional development in teaching and subject expertise.

Institutional

Responsibilities

As a permanent member of faculty, the successful candidate will also contribute to the broader academic and institutional activities of ISBF, including:

Academic administration and programme coordination.

Student recruitment and academic outreach initiatives.

Faculty development activities.

Workshops, seminars and guest lectures.

Industry and academic engagement initiatives.

Admissions, orientation and other student-facing activities where required.

Institutional events and other responsibilities assigned as part of the faculty role.

What We Look

For

We are looking for an academic who combines strong theoretical knowledge, excellent teaching ability and a genuine commitment to undergraduate education.

The ideal candidate should be able to explain mathematically rigorous concepts such as probability distributions, likelihood, estimation and hypothesis testing clearly and intuitively, while maintaining the academic depth expected of a University of London Level 5 course. The subject guides specifically position these courses as foundational preparation for specialised study in statistics, actuarial science and econometrics.

Candidates with backgrounds in Statistics, Mathematical Statistics, Probability, Econometrics or Mathematics, particularly those with solid undergraduate teaching experience, are encouraged to apply.

If you are passionate about statistics, quantitative reasoning and developing students' ability to apply rigorous statistical methods to real-world problems, we'd love to hear from you.

Interested candidates are invited to apply with their CV and a brief cover letter.

📌 Permanent Faculty Position: Machine Learning/Advanced Statistics (Delhi)
🏢 Indian School of Business and Finance
📍 Delhi