Bayesian factor models are widely used for dimensionality reduction and pattern discovery in high-dimensional datasets across diverse fields. These models typically focus on imposing priors on factor loading to induce sparsity and improve interpretability. However, factor score, which plays a critical role in individual-level associations with factors, has received less attention and is assumed to have standard multivariate normal distribution. This oversimplification fails to capture the heterogeneity observed in real-world applications. We propose the Sparse Bayesian Factor Model with Mass-Nonlocal Factor Scores (BFMAN), a novel framework that addresses these limitations by introducing a mass-nonlocal prior for factor scores. This prior provides a more flexible posterior distribution that captures individual heterogeneity while assigning positive probability to zero value. The zeros entries in the score matrix, characterize the sparsity, offering a robust and novel approach for determining the optimal number of factors. Model parameters are estimated using a fast and efficient Gibbs sampler. Extensive simulations demonstrate that BFMAN outperforms standard Bayesian sparse factor models in factor recovery, sparsity detection, and score estimation. We apply BFMAN to the Hispanic Community Health Study/Study of Latinos and identify dietary patterns and their associations with cardiovascular outcomes, showcasing the model's ability to uncover meaningful insights in diet.

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