Reasoning-intensive ranking models built on large language models (LLMs) have made notable progress, but existing approaches often rely on very large-scale LLMs and explicit chain-of-thought reasoning, resulting in high computational cost and latency that limit real-world use. To address this, we propose TFRank, an efficient pointwise reasoning ranker based on small-scale LLMs. TFRank combines chain-of-thought data, multi-task training, and fine-grained score supervision to improve ranking performance. Furthermore, with a think-mode switch and pointwise format constraints, TFRank leverages explicit reasoning during training while delivering precise relevance scores for complex queries without generating reasoning chains at inference. Experiments show that TFRank achieves performance comparable to models with four times more parameters on the BRIGHT benchmark, and demonstrates strong competitiveness on the BEIR benchmark. Further analysis shows that TFRank achieves an effective balance between efficiency and performance, providing a practical solution for integrating advanced reasoning into real-world systems.